Combination Genome-wide Association Study and Bulked Segregant Analysis Reveals the Loci Controlling Stalk Fiber Traits Related to Stalk Rot in Maize

Combination Genome-wide Association Study and Bulked Segregant Analysis Reveals the Loci Controlling Stalk Fiber Traits Related to Stalk Rot in Maize | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Combination Genome-wide Association Study and Bulked Segregant Analysis Reveals the Loci Controlling Stalk Fiber Traits Related to Stalk Rot in Maize Yu Liu, Xin Wang, Yuhang Yin, Zhigang Shang, Zhaoxia Li, Seol Ki Paeng, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7255302/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Stalk rot is a widespread soil-borne disease that severely impacts both the yield and quality of maize. The occurrence of stalk rot is closely related to the structure and composition of the stalk. Microstructure analysis revealed that the disease-resistant inbred line (QM1) presented a more organized and tidy arrangement of vascular bundles, along with an increased area and greater cortex thickness than did the susceptible inbred line (HZ4). To explore the regulatory loci of stalk rot-associated stalk fiber components, genome-wide association studies (GWASs), bulk segregation analysis (BSA), and localization approaches were used to explore the regulatory loci of maize stalk rot-associated stalk fiber components. A total of 20 SNPs appeared to be connected in controlling maize’s disease defense response. PZE-105083600 at Chr5, and PZE-107034235 at Chr7 were identified as candidate loci for regulating lignin content. The fluorescence quantification results revealed that the expression of two genes, PZE-105083600 and PZE-107034235 , was significantly greater in the disease-resistant lines than in the disease-susceptible materials. The results revealed that the expression of these two genes was significantly greater in the disease-resistant lines than in the disease-susceptible materials. This study is highly important for further investigations of regulatory genes related to the fiber composition of maize rot-associated stalks and the selection and breeding of disease-resistant varieties. Stalk fiber component Genome-wide association study (GWAS) Bulked segregant analysis (BSA) lignin BLINK Maize stalk rot Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Maize ( Zea mays L. ) is a globally significant food crop that is crucial for ensuring food security [ 1 ]. With the development of agriculture, plant diseases have become increasingly important factors affecting crop yield and economic efficiency. Among several disease-causing pests, Fusarium spp . Fungi and the diseases they cause pose the greatest threat to seed yield and quality[ 2 ]. Stalk rot is one of the most devastating soil-borne diseases in maize. The increasing incidence of this disease, coupled with the effects of climate change, has led to continued yield limitations in maize cultivation. Early field practice has shown that the stalk structure is closely related to stalk rot; therefore, studying the genetics of this association is imperative for enhancing resistance to this detrimental disease. The occurrence of maize stalk rot is closely related to the structure of the maize stalk. The plant cell wall serves as the primary defense against pathogen invasion, while the fibrous component of the stalk enhances the mechanical integrity of the secondary cell wall, thereby conferring resistance to stalk rot[ 3 , 4 ]. Moreover, the strength of the maize stalk is related to the cellulose and lignin contents of the relevant components of the stalk[ 5 ]. Lignin is an important component of the cell wall of vascular plants, imparting strength and rigidity to the cell wall and increasing the stiffness and mechanical strength of the plant body by filling gaps between cellulose structures[ 6 – 8 ]. As a crucial factor, lignin content influences plant resistance to pathogen infection and enhances disease resistance[ 9 , 10 ]. The lignin content of highly resistant maize varieties is consistently greater than that of moderately resistant varieties, both before and after inoculation with Fusarium [ 11 ]. Moreover, the lignin content tends to increase during plant-induced disease resistance, which is consistent with elevated phenylalanine lyase and peroxidase activity, indicating the significant role of increased lignin content in plant disease resistance[ 12 , 13 ]. In addition, studies have been conducted to elucidate the genetic basis of stalk rot resistance in detail and to identify several quantitative trait loci (QTLs) and genes for resistance traits[ 3 ]. Genome-wide association studies (GWASs) have been widely used for functional gene discovery and have yielded many associations between markers and various complex traits[ 14 ]. Compared with traditional linkage mapping, these methods offer the advantages of reducing study time and increasing the number of detected alleles[ 15 ]; in maize, this approach has been effectively used to detect many QTLs or genomic regions that confer resistance to important maize diseases[ 16 ]. For example, Oluk et al. (2016) conducted a GWAS using an association population comprising 274 maize inbred lines and successfully identified three loci associated with rust resistance in maize[ 17 ]. Ade et al. (2015) localized a sorghum stalk rot resistance-associated locus on chromosome 9 via the GWAS method[ 18 ]. Jing et al. (2021) performed a GWAS using the genome-wide distribution of 22,048 SNPs in soybeans and discovered that 18 SNPs associated with Sclerotinia stalk rot (SSR) resistance were distributed across 12 different soybean chromosomes[ 19 ]. Advancements in sequencing technology have facilitated the integration of BSA-seq technology with GWAS as a highly regarded and powerful tool for localizing QTLs. Bulk segregation analysis (BSA) is primarily applied to control quality trait loci but can also be utilized for quantitative traits controlled by main effect loci [ 20 ]. The BSA effectively controls false positives and enhances the accuracy of GWAS results. For example, Gyawali et al. (2019) obtained three significant SNP loci by combining a GWAS and BSA for maize height via 5000 pi269743 maize populations[ 21 ]. Therefore, in the present study, we used a combination of BSA and GWAS for the correlation analysis of lignin QTLs. The occurrence of maize stalk rot is closely related to the structure of maize fibers. When invaded by pathogens, the plant cell wall is used as the main line of defense against pathogen infestation and influences the strength of the stalk by affecting the tissue structure of the cell wall, which has a certain effect on the occurrence of stalk rot. The cellulose synthase gene family plays an important role in regulating secondary cell wall formation, with ZmCesA10 , ZmCesA11 , and ZmCesA12 interacting with each other[ 22 ]. An RIL population of 200 plants was constructed via the maize inbred lines B73 and By804, five loci controlling lignin were located on chromosomes 1, 2, 6, and 7, and three cellulose-related loci were also identified on chromosomes 1 and 6[ 23 ]. Similarly, in a population of 163 RILs constructed by Rio and WM13, four loci that are important for regulating lignin content were distributed on chromosomes 1, 2, 4, and 8, and a total of five loci related to cellulose content were identified in bins 1.07, 1.11, 3.04, 4.04, and 8.02. In addition, with respect to hemicellulose content, related loci were present at one location in bin 1.07, bin 1.11, bin 4.04, and bin 8.02[ 24 ]. The relationship between the stalk fiber fraction and maize rot has not previously been fully characterized. Although we have obtained some mapping results and genetic markers for stalk fiber fractions and maize resistance, there is still a need to explore in depth the genetic patterns of stalk fiber composition and its association with stalk rot. These findings are highly important for improving the disease resistance of crops and promoting agricultural production. In this study, 153 inbred maize lines from 2019 and 2020 were used as materials, and phenotypic analysis was carried out via inoculation of stalk rot pathogens in the field. The regulatory sites of stalk fiber components associated with maize stalk rot were subsequently explored via GWAS and BSA. Finally, we identified candidate genes regulating lignin content, which provides theoretical support for further exploration of the relationships between stalk rot and stalk components, analysis of the genetic rules and related genes of stalk fiber components, and study of stalk rot resistance in maize. Methods Plant materials and field management A total of 153 maize inbred lines derived from four subgroups were analyzed in this study: China LvDahonggu, TangSiPingtou, Tropical Species Group, P Species Group, and sweet glutinous maize. The plant material and phenotypic analyses were consistent with those used in previous methods[ 25 ]. We obtained 200 F2 individuals via recombinant selection using the disease-resistant line QM1 and the disease-susceptible line Huangzao4 (HZ4) as parents. The resulting recombinant F 2:3 (♀QM1 × ♂HZ4) families obtained through self-pollination were genotyped for further analysis. These selfed lines were either collected or bred by the maize molecular breeding team at Qingdao Agricultural University. In this study, the association materials were planted in 2019 and 2020, whereas a BSA population was planted in 2021 in the Laiyang test field in China. The maize inbred line culture was conducted via a completely randomized block design with three replications at each experimental site. Each row consisted of 20 maize plants, with 3 m spacing between rows and 0.6 m spacing within rows. All the field management practices adhered to standard agricultural protocols. Inoculation and phenotyping Artificial inoculation was performed via the injection method in this study (Fig. 1 -C). Fusarium graminearum was cultured in Petri dishes containing potato dextrose agar medium at 26℃ in a dark chamber. One week before inoculation, the mung beans were boiled in water for 10 min and filtered through gauze. Then, the medium was transferred into flasks and autoclaved at 121℃ for 30 min. Five to six clusters approximately 1.5 ㎝ in diameter were used to inoculate 100 ml of mung bean medium, which was rotated at 180 rpm, continuously oscillated for 5‒7 days, and incubated at 28℃ until spores were produced from the solution. The spores were filtered and prepared for field inoculation. When the maize reached the staminate stage, we injected 1 ml of pathogen mixture into the second node of the above-ground stalk via a sterile syringe to ensure a slow and uniform inoculation rate to prevent spillage of the pathogen[ 25 ]. After injection, the inoculation site was sealed with a breathable adhesive to prevent contamination by other pathogens and rain[ 26 ]. We scored plants that exhibited typical stalk rot symptoms at the milk-ripe stage. We longitudinally split the stalks at the upper and lower internodes and then observed the phenomenon after the plants were inoculated with the pathogen. The degree of infection in each stalk was classified on a scale of 1–6 on the basis of the extent of browning in its internal tissues and the size of the decayed area (Fig. 1 -D). Materials of grades 1–3 exhibit resistance, whereas those of grades 4–6 display sensitivity[ 27 ]. A cross section of a stalk with a score of 6 exhibited complete decay and a deep brown color (100%), a score of 5 represented 80% decay and a brown color, a score of 4 represented a 50% brown color, a score of 3 represented a 30༅ brown area around the inoculation site, a score of 2 represented a 10༅ brown area, and a score of 1 represented almost no symptoms of rot. On the basis of Chiang et al.’s work, the grading values were converted to disease severity index (DSI) values , which were calculated as DSI (%)= [sum (class frequency×score of rating class)]/[(total number of plants)×(maximal disease index)]×100[ 28 ]. To increase the accuracy of the results by mitigating the influence of environmental and genetic effects, we employed the lme4 package in R software ( https://www.r-project.org ) to fit the best linear unbiased estimates (BLUE) value. The coefficients of kurtosis, skewness, and coefficient of variation (CV) for each trait were calculated via SPSS software and Excel 2019 and recorded in a table. The CV was calculated via the following formula: CV=(SD ÷ Mean×100%), where SD is the standard deviation. To generate a normal distribution map, the phenotypic data were subjected to normality testing analysis (Kolmogorov–Smirnov method) and distribution-fitting analysis via Origin mapping software. Microscopic observation of the microstructure of the stalks To examine the microstructure of the stalks, samples were collected from the second internode at the stalk base. These samples were subsequently precisely sectioned into 1.0 cm × 0.2 cm squares via a sharp blade and then immersed in FAA fixative for fixation. After fixation, the slices were sectioned via a paraffin slicer (Leica Microsystems Trading Co.). The wax blocks were stabilized and repositioned to a thickness of 8 µm. After sectioning, the wax slices were gently placed in ultrapure water preheated to 42°C for unfolding and retrieval via adsorbent slides. They were then placed on a dryer at 37°C for drying and set aside. Next, after the material was rehydrated, it was stained with 1% TBO (Solarbio) and visualized via a positive fluorescence microscope (Leica Microsystems CMS GmbH). Determination of fiber composition After the disease resistance of the maize in the field was assessed, the recovered samples were placed in an oven for processing. The stalks were sterilized at 105°C for 30 min and then dried continuously at 65°C before being passed through a 100-mesh sieve after pulverization via a universal grinder. Spectral analysis of the cellulose, hemicellulose, and lignin in the maize stalk was performed via Fourier transform near-infrared spectroscopy. For NIR analysis, samples with less than 10% moisture that were dry and well mixed were used and scanned in a cup with a diameter of 40 mm. Each scan collected an average of three spectra to minimize sample heterogeneity error. The collected spectra were within the range of 4000 ~ 10000 nm. Additionally, the measured sample spectra were converted into numerical data via the proposed NIR spectral conversion model. Sequencing and genotype analysis To identify resistance genes in tropical maize, a new association panel containing 153 individuals was constructed for disease analysis from the 384 panel (55k)[ 29 ] and tropical germplasm (20K)[ 30 ]. By integrating two kinds of chips in 153 lines, 5585 markers were obtained. Quality checking (QC) was conducted with the method used in our previous study[ 1 ]. A total of 4,666 markers remained after QC. These markers contained multiple mutation types (more than 2,600 markers) in the allele, such as PZE-108075114 (chromosome 8, position 129390898), which included 9 mutation types: CC, TT, AA, GG, CT, AG, GT, CA, and CG. Here, we used GAPIT's new function, GAPIT HMP amplification, to code all the mutations as individual numeric values (1 indicates that the genotype exists, and 0 indicates that it does not exist). This method is similar to Johannes’s report in 2017 (Genomic prediction with epistasis models: on the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE)). Finally, 10,100 new markers were used for downstream analysis. GWAS Analysis The genotype data of the same population were quality checked in our previous study, and principal component analysis (PCA) was conducted with the PCA package in GAPIT[ 25 ]. A plot of the first two principal components (PCs) was created to visualize the possible population stratification among the samples (Supplementary Figures S1 ). The P value was set as 1×10 − 3 for the genome-wide significance threshold (P = 1/n; n is the effective number of SNPs). To determine the best model for the component analysis of maize disease, three software programs (FarmCPU[ 31 ], BLINK[ 32 ] and RTM-GWAS[ 33 ]) were used for GWAS analysis. Fixed and random model circulating probability uniform (FarmCPU) and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK) analyses were conducted with an R package (GAPIT), and the significant markers and P value distributions were visualized via Manhattan plots and quantile–quantile plots (Q‒Q plots). Linkage mapping by bulked segregant analysis On the basis of the lignin content of the F 2:3 population, we selected 25 individuals with the highest and lowest lignin contents for screening. DNA was extracted separately via a high-throughput CTAB method, and equal amounts of each DNA sample were mixed into strong and weak pools. These samples were then sent to Beijing Baike Biotechnology Co. for sequencing. Additionally, the DNA of four samples (QM1, HZ4, the strong pool, and the weak pool) was randomly fragmented into 350 bp fragments via ultrasonication. The DNA from four samples (QM1, HZ4, strong pool, and weak pool) was randomly fragmented into 350 bp fragments via ultrasound technology. The fragments subsequently underwent end repair and nucleotide (A) overhang addition. These modified fragments were then ligated to the sequencing adapter via T4 DNA ligase and subsequently amplified via PCR. After the construction of the libraries, the DNA from the extremely mixed pools in the F 2:3 family line was sequenced. Clean reads were aligned to the maize reference genome (B73 RefGen v3) via Burrows–Wheeler Aligner software, with a read length depth of less than 6 and an SNP index of less than 3. All indices for SNP/indel/SV and difference values for the two extreme pools were calculated[ 34 ]. To determine the physical location of each SNP, analysis of variance (ANNOVAR) was used for SNP location annotation. Intervals exceeding the threshold at the 95% confidence level were subsequently selected on the basis of ∆ (SNP index). The InDel (insertion and deletion)/SNP site sequences were subsequently extracted from the resequencing data for molecular marker development. The PCR products were separated via 8% denaturing polyacrylamide gel electrophoresis, followed by silver staining, and genotype data were collected accordingly. Primer 5 software was employed to design insertion–deletion markers on the basis of the QM1 and HZ4 resequencing data. Subsequently, individuals exhibiting recessive traits were chosen from the QM1 × HZ4 F 2:3 population planted in 2020 for genotyping purposes. Finally, Ici mapping software was utilized to construct a chain map with an LOD threshold set at 2.5. Linkage mapping of a specific site was conducted to verify the loci from the BSA[ 11 , 35 ]. SNP annotation Candidate genes associated with target traits were identified via the MaizeGDB database (B73_RefGen_v3) on the basis of the physical positions of significantly associated SNPs. The molecular marker database in MaizeGDB was utilized to examine genes corresponding to each SNP locus, and functional annotations of candidate genes were predicted through NCBI. Quantitative real-time PCR (qRT–PCR) The expression patterns of candidate resistance genes in various maize cultivars under Fusarium graminearum infestation were analyzed via qRT‒PCR. Samples were collected at 0, 24, 36, and 48 h post inoculation and immediately frozen in liquid nitrogen for subsequent experiments. An RNA Extraction Kit (TAKARA) was used for RNA extraction, followed by reverse transcription into cDNA. The primers used were as follows: Actin-1 ( GRMZM2G126010 ) F: 5'-GTCCATGAGGCCACGTACAA-3', R: 5'-CCGGACCAGTTTCGTCATA-3'; PZE-105083600 F: 5'-AGCTCGTGAGAAGCAAACCT-3', R: 5'-CATTTGACCTTGGGACACGA-3'; PZE-107034235 F: 5'-GAAGTGCTGCATGATATGTGGG-3', R: 5'-GCTGCGAGTCGAACCTT − 3'. The qRT‒PCRs were conducted on a T100TM thermocycler (Bio-Rad) with a 2× ChamQ Universal SYBR qPCR Master Mix Kit (Vazyme). The cycling conditions included initial denaturation at 94℃ for 30 s, followed by amplification over the course of 40 cycles of denaturation at 95℃ for 10 s, annealing at 55℃ for 10 s, and extension at 72℃ for 30 s. Actin-1 served as an internal reference gene in the qRT‒PCR analysis. Experiments were performed on disease-resistant and disease-sensitive materials and independently repeated at least three times to ensure test accuracy. The ratio of expression levels was calculated via the following formula: R = 2^ − CT (target) – CT ( actin −1) [ 36 ]. Results Phenotype Statistics In this study, 153 samples from the Reid, LvDahonggu, group P, and TangSiPingtou subgroups, as well as some tropical lines and sweet Ceres inbred lines, were analyzed. As shown in Table 1 , we analyzed the continuous change trends in the composition of stalk fibers associated with stalk rot, revealing a wide range of variations in lignin, cellulose, and hemicellulose contents. The lignin content varied from 3.07–43.22%, while the cellulose content ranged from 7.07–40.40%. Except for the slightly skewed BLUE value for lignin (0.849), the absolute skewness values for all the other data were less than 1, indicating stable distributions with balanced kurtosis values (Fig. 2 A-I). These results suggested that most of the trait distributions among the inbred maize lines presented normal distribution patterns suitable for QTL mapping. After comprehensive analysis of the data, it was determined that most of the traits in these inbred lines exhibited a stable distribution and adhered to customary distribution laws, indicating their reliability and stability. However, certain traits presented significantly expressed eigenvalues, which may be associated with the genotype of the maize self-inbred lines. Therefore, these prominently expressed eigenvalues can be further investigated to better understand the genetic factors underlying them. Table 1 Phenotypic identification of stalk-associated fiber components. Trait Year Min Max Mean SD* CV* Skewness Kurtosis ADL* 2020 3.07 43.22 20.19 7.58 37.53 -0.17 -0.32 2020 1.27 40.32 12.97 8.29 63.9 0.57 0.1 BLUE 5 42 16.58 6.78 40.89 0.85 0.69 CEL* 2020 7.07 40.4 24.01 6.9 28.72 -0.15 -0.54 2020 12.35 48.02 35.51 8.49 25.32 -0.7 -0.44 BLUE 10 42 28.76 6.43 22.35 -0.67 -0.03 HCEL* 2020 14.78 30.71 23.74 3.23 13.62 -0.1 -0.55 2020 17.48 30.79 25.99 3.09 11.94 -0.76 -0.42 BLUE 18 30 24.87 2.52 10.13 -0.14 -0.56 Traits. ADL: lignin; CEL: cellulose; HCEL: hemicellulose.; SD, standard deviation; CV, coefficient of variation. CV=(SD ÷ Mean×100%) Correlation analysis of related fiber components in the stalks To ensure the accuracy and consistency of our findings, we conducted a two-year analysis of significant correlations among three fiber compositions related to maize stalks via SPSS 20.0 software. The results, presented in Fig. 3 , revealed significant correlations between DSI and ADL and between CEL and HCEL, as well as highly significant correlations between ADL, CEL, and HCEL in self-fertilized line populations during 2019 and 2020. Additionally, DSI exhibited a highly significant negative correlation with ADL in both years (correlation coefficients of -0.219 and − 0.400; P < 0.01 ), indicating the crucial role of ADL in maize stalk rot. Conversely, the CEL and HCEL were positively correlated with the DSI , suggesting a trade-off relationship between these indicators. In general, higher levels of ADL and lower levels of CEL correspond to increased disease resistance. We found a significant correlation between DSI and the three components of SAFs, indicating that regulating SAFs is necessary for effective control of disease resistance. Microstructure analysis To explore the differences in stalk tissue structure between resistant (QM1) and susceptible (HZ4) lines, paraffin sections were obtained from the central region of the second basal internode of the above-ground segment at maturity and examined from a cross-sectional perspective (Fig. 1 A, B). The results revealed that the area of vascular bundles in QM1 was significantly greater than that in HZ4, whereas the number of vascular bundles in QM1 was lower. Additionally, the distribution of vascular bundles in QM1 was more uniform and orderly than that in HZ4. Furthermore, the cell wall thickness of QM1 was significantly greater than that of HZ4. The histological structures of the stalks differed significantly between the resistant and sensitive materials. These findings indicate substantial histological structural disparities between resistant and susceptible materials, which might influence stalk rot development through their effects on stalk strength. GWAS analysis To assess SNP locus correlations between maize stalk rot and stalk fiber components (ADL, CEL, and HCEL), we used three different GWAS analysis models and performed a two-year GWAS of three types of trait data in three approaches (Fig. 4 and Supplementary Figures S2 –S4). The quantity of each genotype was checked in our previous study[ 25 ]; the minor allele frequencies of 4658 markers were at least 5%, and the fitted model revealed that the LD decayed to 0.1 at 300 Mb[ 25 ]. On the basis of the PCA results, the panel of accessions was clustered into four groups; they exhibited distinct separation along the first principal component (PC1), the second principal component (PC2), and the third principal component (PC3) axes (Supplementary Figures S1 ). To identify key genes associated with stalk fiber composition, the significance threshold was set at 10 − 3 . The quantile‒quantile (Q‒Q) plots obtained via the three methods revealed that the population structure and correlation between traits in the association groups were well controlled. Through comprehensive analysis via three GWAS packages (FarmCPU, BLINK, and RTM-GWAS), a total of 192 SNP loci significantly associated with stalk fiber component traits were identified across diverse analysis models. These SNPs correlated with ADL (77), CEL (62), and HCEL (53). FarmCPU analysis revealed a total of 33 SNPs, whereas BLINK presented the highest detection rate, with 116 SNPs. The RTM-GWAS approach identified 43 SNPs. Upon comparing the results obtained from different models, we consistently identified 20 SNPs via two or more analytical methods (Table 2 ). Among these markers, twelve SNPs loci located on chromosomes 1, 2, 3, 5, 6, 7, 9, and 10 were associated with ADL; another set of twelve SNPs loci situated on chromosomes 1, 2, 3, 5, 6, 7, and 10 showed associations with the CEL content. Additionally, thirteen HCEL-associated SNPs loci were discovered on chromosomes 1, 2, 3, 4, 5, 8, 9, and 10. Notably, the two most stable SNPs ( PZE-101211038 and PZE-105021925 ) were consistently detected across most environments by all three models, and these two loci were significantly correlated with the ADL, CEL, and HCEL traits. This approach, which employs a combination of multiple GWAS models, enables enhanced screening capabilities, resulting in improved accuracy of our findings. Table 2 SNPs associated with resistance to stalk fiber traits related to stalk rot in maize according to the four models. SNP maker Chr Position Phenotype Method PZE-103093664 3 150818560 ADL、CEL B、F PZE-105048147 5 37578032 ADL、CEL B、F PZE-105083600 5 99732524 ADL、CEL、HCEL B、F PZE-106053973 6 104827794 ADL、CEL B、F PZE-102091357 2 95313459 ADL、CEL、HCEL B、F PZE-110009225 10 6688491 ADL、CEL B、F PZE-110013761 10 12806853 ADL、CEL、HCEL B、F PZE-110047844 10 89464132 ADL、CEL B、F SYN15334 3 3864123 HCEL B、F PZE-104025517 4 30059447 HCEL B、R PZE-101247830 1 292211608 CEL F、R PZE-104099090 4 174587871 HCEL F、R SYN13541 5 45863137 HCEL F、R PZE-108067299 8 117640295 HCEL F、R PZE-107121188 7 160869730 ADL、CEL B、F、R PZE-101178823 1 223152986 HCEL B、F、R SYN5650 3 4641951 HCEL B、F、R PZE-105021925 5 10393432 ADL、CEL、HCEL B、F、R PZE-109090062 9 133448843 ADL、HCEL B、F、R PZE-101211038 1 259863931 ADL、CEL、HCEL B、F、R Chr, chromosome; F; FarmCPU; B: BLINK; R: RTM-GWAS BSA-seq analysis To validate the candidate regions obtained from the GWAS, an integrated BSA and resequencing approach was used. Fifty plants with extreme F 2:3 phenotypes from the cross QM1 × HZ4 were selected, and their DNA was pooled together to construct two DNA bulks: the BR-bulk and the BQ-bulk (Supplementary Table S1 ). The total amount of clean data generated was 279.07 G, with an average ratio of 92.03% and an average GC content of 46.86% (Supplementary Table S1 ). After screening, a total of 35,999 SNPs and 535,954 InDels were identified compared with the reference genome MaizeGDB database (B73_RefGen_v3). The offspring SNP frequency differences were subsequently integrated to calculate the SNP index ∆ (SNP index) for the pooled populations of BR-bulk and BQ-bulk. Differences in SNP indices (ΔSNP indices) were determined via a sliding window size of 1000 kb with a step size of 10 kb across ten chromosomes. On the basis of the ΔSNP index values, two candidate intervals were identified: Chr 3, 177.8–227.0 Mb, and Chr 5, 24.1–154.7 Mb (Fig. 5). (E) Bin Left Marker Right Marker LOD PVE (%) Add Dom 5.04 92131681 116615470 14.87 15.79 -8.69 -9.20 5.04 92131681 116615470 14.04 15.90 -9.14 6.38 5.04 92131681 116615470 13.13 15.78 -8.87 -8.81 5.04 116615470 116704059 17.25 16.60 3.02 -18.04 Figure 5. The localization of stalk fiber traits was accomplished by employing the BSA-seq technique in conjunction with indel molecular markers. (A): Distribution of SNP index values for the BR-bulk; (B): distribution of SNP index values for the BQ-bulk; (C): distribution of ∆SNP index values. The horizontal coordinates are the chromosome names, the colored dots represent the calculated SNP index (or ΔSNP index) values, and the black lines are the fitted SNP index (or ΔSNP index) values, where the red line represents the threshold line with a confidence level of 0.99, the blue line represents the threshold line with a confidence level of 0.95, and the green line represents the threshold line with a confidence level of 0.90. (D): Linkage map with Indel markers on chromosome 5; (E): QTL mapping for resistance to maize stalk fiber lignin. Two potential candidate genes identified via GWAS and BSA To further validate the QTL loci, InDel primers were developed on the basis of the resequencing data of chromosome 5 from 24.1–154.7 Mb. Initially, we used 56 pairs of InDel marker primers to detect polymorphisms in the parental lines HZ4 and QM1 as well as in the F 2:3 population. Among these primers, only 13 produced definitive genotyping results for both parents and the F 2:3 generations. We genotyped 42 resistant individuals from the F 2:3 population via these 13 primer pairs (Supplementary Table S2 ). Local linkage genetic maps were constructed via Ici mapping software to analyze the relationships between lignin content and genetic loci, which led us to identify two significant QTLs on chromosome 5 with LOD values ranging from 13.13 to 17.25 and contribution rates ranging from 15.78–16.60%. When the LOD threshold was set to 2.5, four QTL regions were identified on chromosome 5, with LOD values ranging from 14–18% (Fig. 5). Finally, BSA and GWAS screening methods identified a candidate gene, LOC100276302 ( GRMZM2G119890 ), on chromosome 5 with a PZE105083600 marker. Through GWAS screening, approximately 300 kb away on chromosome 7, the marker PZE-107034235 was identified, and the candidate gene was LOC542735 ( GRMZM2G131836 ), which is a Cinnamoy-CoA reductase ( CCR2 ) gene that plays an important role in the lignin synthesis pathway. To validate the association of candidate genes with lignin, we conducted fluorescence quantitative analysis on pivotal candidate genes identified through the GWAS and BSA. The LOC100276302 gene in the PZE-105083600 locus and the LOC542735 gene in the PZE-107034235 locus were detected via qRT‒PCR in different materials after inoculation with F. graminearum , and the results are shown in Fig. 6 . The results of the present study revealed that the LOC100276302 gene at the PZE-105083600 locus in QM1 and QM2, which are highly resistant to stalk rot, gradually increased over time and was significantly upregulated at 48 hr. The expression of the candidate gene was elevated approximately 2.25-fold and 1.8-fold in QM1 and QM2, respectively, compared with that at 0 hr. The expression of the LOC542735 gene increased significantly over time in the disease-resistant lines, by 1.85-fold and 1.79-fold at 48 h, respectively, which was much greater than that in the susceptible lines. On the basis of the above results, we inferred that the candidate genes LOC100276302 and LOC542735 were closely related to maize stalk rot production and fiber composition. Discussion The structure and composition of maize stalks are intricately linked to the development of stalk rot. In this study, we employed a combination of GWAS and BSA-seq methodologies to identify genes associated with maize stalk rot-related fiber composition, which were further validated through fluorescence quantification. The results demonstrated the efficacy of this integrated approach in pinpointing relevant candidate genes. Additionally, the three GWAS methods successfully identified SNPs and novel loci that corroborated previous findings[ 37 , 38 ], thereby offering novel markers and genes for comprehensive investigations into maize stalks and providing theoretical support for enhancing disease resistance in maize varieties. Lignin, as a constituent of stalk fibers, may play a pivotal role in conferring resistance to maize stalk rot[ 39 , 40 ]. Microscopic observations of paraffin sections obtained from the second substrate internode of the resistant (QM1) and sensitive (HZ4) lines revealed significant histological differences between these two materials. Specifically, compared with HZ4, QM1 exhibited a significantly thicker cortex, which was characterized by fewer but larger vascular bundles, which is consistent with previous findings. We postulated that the disparity in disease resistance could be attributed to variations in fiber composition between these materials. The resistant line of QM1 might recognize fungal infection and initiate defense mechanisms to establish an impervious barrier against pathogen invasion. Upon the penetration of Fusarium graminearum through the cell membrane, cell wall reinforcement is rapidly activated [ 41 ], involving multiple genes responsible for increasing the lignin content and other constituents[ 42 , 43 ]. Lignin, one of the primary constituents of the cell wall, enhances the cellular structural barrier and confers protection against pathogens[ 44 ]. Furthermore, lignification, or the defense response, represents a crucial mechanism employed by plants to counteract pathogenic attacks. The promotion of lignification reinforces the mechanical strength of the cell wall while reducing its susceptibility to the degrading enzymes produced by Fusarium graminearum during the infection process and response to Fusarium head blight[ 45 ], as well as limiting the diffusion of pathogenic compounds[ 46 ] impeding pathogen invasion. These collective findings, in conjunction with our research outcomes, underscore the integral role played by lignin in mediating plant resistance against pathogens. In this study, we used three different pieces of analytical software to perform GWASs for data on cellulose, lignin, and hemicellulose contents measured over two years. A total of 192 SNP loci were detected, and 20 markers were detected via at least two methods. A total of 33 SNP loci closely related to maize stalk rot were detected via FarmCPU analysis. These SNP loci were distributed across the 10 chromosomes of maize. Specifically, a maximum of 6 loci were detected on chromosomes 1 and 5, whereas only 1 SNP locus was detected on chromosomes 4, 6, and 8, such as PZE-101211038 , PZE-103093664 , PZE-105048147 , and PZE-106053973 , which were also detected via BLINK. The marker PZE-104025517 was screened with both BLINK and RTM-GWAS. A total of 116 SNPs were identified with BLINK, and the maximum number of SNP loci detected on chromosome 3 was 19. Chromosome 7 had the second highest number (16), on which the PZE-105021925 marker was co-identified via two other methods. A total of 43 SNP loci were detected with RTM-GWAS, while chromosome 1 had the highest distribution. In summary, the maximum number of markers was detected on chromosome 1, and the fewest were detected on chromosome 6 with BLINK, FarmCPU, and RTM-GWAS together. This finding indicated that candidate genes for the target traits may be present on this chromosome. The number of ADLs detected in BLINK reached 46. A total of 19 sites were detected via RTM-GWAS, and at least 12 sites were detected via FarmCPU. This finding suggested that BLINK was more effective in detecting relevant sites than FarmCPU was. RTM-GWAS was able to detect potential SNPs more comprehensively, helping to avoid anything that might be missed. On chromosome 10, PZE-110009225 , which encodes an LHW transcription factor ( GRMZM2G031656 ), was detected simultaneously by two models. Previous studies in Arabidopsis have demonstrated that LHW transcription factors play pivotal roles in the differentiation and proliferation of xylem cells, with their activity being regulated by auxin signaling[ 47 ]. A study by Jos R. Wendrich et al. (2020) also indicated that the LHW/TMO5 complex regulates the biosynthesis of cytokinin, thereby influencing the proliferation and division of vascular cells. Since the development of vascular tissues, including xylem, is closely related to the response of root hair cells, the LHW/TMO5 complex may have an impact on the development of xylem[ 48 ]. Moreover, PZE-108067299 on chromosome 8, which encodes WRKY transcription factor 38 ( GRMZM2G432583 ), was associated with HCEL traits and detected with two methods. Studies have shown that WRKY transcription factors play crucial roles in plant defense responses against pathogens. For instance, AtWRKY33 has been demonstrated to be essential for resistance against necrotrophic fungal pathogens such as Botrytis cinerea, by regulating the expression of defense-related genes and phytoalexin biosynthesis[ 49 ]. Similarly, OsWRKY13 in rice mediates resistance to both Magnaporthe oryzae and Xanthomonas oryzae pv. oryzae by modulating the expression of defense-related genes through salicylic acid and jasmonic acid signaling pathways[ 50 ]. In pepper , CaWRKY58 has been shown to negatively regulate resistance to Ralstonia solanacearum , highlighting the diverse roles of WRKY transcription factors in different plant species [ 51 ]. These findings underscore the importance of WRKY transcription factors in mediating plant immune responses and suggest that GRMZM2G432583 , encoding WRKY transcription factor 38 , may similarly contribute to defense mechanisms in maize. In this study, we employed three GWAS software packages to identify SNP loci associated with the ADL, CEL, and HCEL components, revealing variations across different models. The results of these models revealed that the FarmCPU performed faster than did the other two packages while producing intermediate numbers of SNPs. This is primarily because the FarmCPU divides its model into two parts, i.e., a fixed effect model (FEM) and random effects model (REM), where the FEM includes one test marker at a time while utilizing other associated markers as covariates to control false positives; the REM optimizes these associated markers through affinity definitions and an optimization process. The computational time required for FEM-REM iterative approaches is linearly related to the sample size and number of markers[ 31 ]. Second, the BLINK software results in the greatest number of detection sites, demonstrating its superior detection capability and utilization. This is because each time a marker is detected, BLINK adds pseudo-QTN as a covariate to control for false positives and reduce false negatives; the more pseudo-QTNs there are, the greater the likelihood that a QTN will be detected[ 32 ]. In addition, BLINK and FarmCPU produce similar results. This is because FarmCPU and BLINK have the same level of performance. Additionally, BLINK detects more SNP loci than FarmCPU does. This is because BLINK is designed to reduce the computation time by replacing the REM with an FEM using the Bayesian information criterion (BIC) and determining the set of pseudo-QTNs that optimally control false alarms and minimize missed alarms. Thus, it compensates for the lack of FarmCPU[ 32 , 52 ]. In addition, compared with FarmCPU, RTM-GWAS detects a greater number of loci. This can be attributed to the two-stage analysis strategy implemented by RTM-GWAS, which effectively reduces the computational complexity of the multilocus model and enables the identification of more significant loci. Additionally, the markers utilized by this software facilitate the detection of polymorphic allelic variation in genome-wide QTLs, thereby enabling the identification of a greater number of SNP loci at a similar level of significance. He et al. also demonstrated that this method successfully identified more SNP loci across 1024 soybean species [ 33 ]. In this study, target trait-related SNPs were detected via the above three methods. We believe that an SNP detected via at least two methods is worthy of further investigation. A total of 20 SNP loci were detected within the two models and exhibited significant concordance with previous findings. Therefore, integrating the results from diverse GWAS models is imperative for obtaining more relevant SNPs[ 53 ]. This methodology has been implemented in several prior studies [ 54 – 57 ].GWAS analysis is a crucial approach for identifying genotype–phenotype associations, and its results are influenced by a variety of factors, such as phenotypic variation, the number of individuals, population structure, and allele frequency[ 58 ]. During the assay, caution must be exercised to avoid false-positive or false-negative results. To increase the certainty of GWAS results, a combination of BSAs serves as a common complementary method to reduce uncertainty and improve accuracy. Previous studies have demonstrated that GWASs and the BSA can mutually complement each other in accurately identifying key regions. For example, four QTLs associated with maize prolificacy were jointly identified through a GWAS of 492 different maize inbred lines combined with a BSA of an F2 population consisting of the few-ear line Zheng58 and multiyear line 647[ 59 ]. Similarly, this combined approach was employed to validate the primary QTL associated with water tolerance in maize[ 60 ]. In addition, three genomic regions related to maize plant height were identified as key QTLs for waterlogging tolerance via this technique[ 21 ]. In this study, we used a combination of GWAS and BSA to obtain lignin-associated gene intervals that were found to be consistent with the lignin-associated gene intervals identified by Guan et al. via an F 1 population generated from 400 maize hybrid combinations[ 61 ]. These findings demonstrated that both GWAS and BSA are effective methods for efficiently identifying key genes for identifying stalk fiber components associated with maize stalk rot. The regulatory mechanisms underlying maize stalk rot remain incompletely understood. In this study, we employed a combination of GWAS and BSA techniques to pinpoint a candidate gene, LOC100276302 ( GRMZM2G119890 ), located on chromosome 5. The results of the quantitative fluorescence analysis revealed that the expression of the LOC100276302 gene located at the PZE-105083600 locus increased significantly with time in the disease-resistant lines and reached the highest level at 48 hr. However, it was only slightly upregulated in the susceptible lines. This finding indicated that the gene played a role when Fusarium graminearum attacked maize. The LOC542735 gene, located at locus PZE-107034235 , was expressed in disease-resistant materials at a similar level to PZE-105083600 , and the disease-resistant material presented 3.9-fold greater fluorescence than did the susceptible material. This suggested that the responses of this gene in disease-resistant and disease-susceptible materials were very different; LOC542735 might play an important role in attack by F. graminearum . Although LOC100276302 was not clearly characterized by NCBI, our analyses revealed a number of features identical to those of the homologous gene, suggesting that LOC100276302 may be involved in cell wall peptide synthesis and relevantly associated with lignin biosynthesis, with functional similarity to its counterpart in Arabidopsis thaliana , AT4G37090 . Furthermore, the key CCR2 gene, LOC542735 on Chr7, was related to biosynthetic pathways in numerous studies. Notably, previous research involving CRISPR/Cas9-generated mutants lacking CCR2 has demonstrated differential effects on the lignin content and growth of CCR2 amino acids[ 62 ]. In addition, a peroxidase-related QTL was detected on chromosome 7[ 63 ], and a CCR gene was also identified, which is in agreement with our results. In summary, these two content-regulated candidate genes provide valuable insights for investigating the relationship between maize stalk fiber composition and resistance to stalk rot disease while guiding optimization strategies for enhancing stalk fiber fractions and disease resistance. Conclusions The present study revealed a significant correlation between maize stalk rot and the composition of stalk fibers. Microstructural analysis revealed that the disease-resistant inbred line QM1 presented a significantly thicker cortex than did the disease-susceptible inbred line HZ4, while it also presented fewer vascular bundles but larger bundle areas than did HZ4. By employing replicated screening methods, including FarmCPU, BLINK, and RTM-GWAS, a total of 20 markers were identified by at least two of these approaches. The integration of GWAS and BSA for stalk lignin content led to the identification of PZE-105083600 on Chr5 and PZE-107034235 on Chr7 as candidate genes regulating lignin content. The fluorescence quantification results revealed significantly higher expression levels of these two genes in disease-resistant materials than in disease-sensitive materials. This study is highly important for further exploration of the regulatory genes associated with maize stalk rot-related stalk fiber composition and the breeding of disease-resistant varieties. Abbreviations GWAS Genome-Wide Association Study BSA Bulked Segregant Analysis QTL Quantitative Trait Locus SNP Single Nucleotide Polymorphism InDel Insertion/Deletion ADL Lignin (Alder) CEL Cellulose HCEL Hemicellulose DSI Disease Severity Index BLUE Best Linear Unbiased Estimates PCA Principal Component Analysis FEM Fixed Effect Model REM Random Effect Model FarmCPU Fixed and Random Model Circulating Probability Unification BLINK Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway RTM-GWAS Randomized Tree Model for Genome-Wide Association Studies LD Linkage Disequilibrium LOD Logarithm of Odds PVE Percentage of Variance Explained Add Additive Effect Dom Dominance Effect qRT–PCR Quantitative Real-Time Polymerase Chain Reaction NIR Near-Infrared CV Coefficient of Variation SD Standard Deviation CTAB Cetyltrimethylammonium Bromide TBO Toluidine Blue O PCR Polymerase Chain Reaction Q‒Q plot Quantile–Quantile Plot MANOVA Multivariate Analysis of Variance ANOVA Analysis of Variance FDR False Discovery Rate MAF Minor Allele Frequency CT Cycle Threshold BIC Bayesian Information Criterion QTN Quantitative Trait Nucleotide GRMZM Genetic Resource of Maize Zea mays CCR2 Cinnamoyl-CoA Reductase 2 WRKY WRKY Transcription Factor LHW LHW Transcription Factor TMO5 TMO5 Transcription Factor Declarations Conflict of Interest The authors declare no competing interests. Ethics, Consent to Participate Not applicable. Consent to Publish declarations Not applicable. Clinical trial number not applicable. Author Contributions Conceptualization: M-AZ , Y-L, X-W and Y-HY; Data curation: Y-HY and Y-L; Funding acquisition: M-AZ, X-L and X-YSo; Investigation: M-AZ and Y-L; Methodology: Y-L and Z-GS; Project administration: Z-GS, Z-XL, S-YL, C-V, H-JZ and X-L; Validation: X-W, M-GL and C-V; Visualization: Y-HY, Z-XL, K-P Seol and H-JZ; Writing – original draft: Y-HY and Z-GS; Writing – review & editing: M-AZ, Y-L, X-W, S-YL, Z-XL, X-L and X-YSo. Acknowledgments We thank the anonymous referees for their critical comments on this manuscript. Funding This research was supported by the National Natural Science Foundation of China (Grant No. 32372165), Shandong Province Seed Improvement Project (SDAIT-01-022-01), Shandong Province Key Research and Development Program (2016GNC110018), and the Academy of Dong Ying Efficient Agricultural Technology. 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Lignin Biosynthesis. Plant Cell. 1995;7:1001–13. 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-7255302","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500482279,"identity":"19ddbb8d-7d52-4136-9c19-23e0c8183e8a","order_by":0,"name":"Yu Liu","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Liu","suffix":""},{"id":500482280,"identity":"e174e439-0dae-413c-96fd-6554d8ac129f","order_by":1,"name":"Xin Wang","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Wang","suffix":""},{"id":500482283,"identity":"2459c7ee-65e2-4804-bee2-f13a16093b4d","order_by":2,"name":"Yuhang Yin","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yuhang","middleName":"","lastName":"Yin","suffix":""},{"id":500482289,"identity":"5eab7c45-e618-4313-bc66-a51384a33acc","order_by":3,"name":"Zhigang Shang","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Zhigang","middleName":"","lastName":"Shang","suffix":""},{"id":500482293,"identity":"267000c6-b5a3-47aa-bf31-4f8e1982bfdf","order_by":4,"name":"Zhaoxia Li","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Zhaoxia","middleName":"","lastName":"Li","suffix":""},{"id":500482296,"identity":"186f3308-47b3-4ec3-8262-72fc13f309bb","order_by":5,"name":"Seol Ki Paeng","email":"","orcid":"","institution":"Gyeongsang National University","correspondingAuthor":false,"prefix":"","firstName":"Seol","middleName":"Ki","lastName":"Paeng","suffix":""},{"id":500482298,"identity":"d0edd5bc-9bc6-477b-8437-d58272b2ea2e","order_by":6,"name":"Mingai Li","email":"","orcid":"","institution":"Fondazione Edmund Mach","correspondingAuthor":false,"prefix":"","firstName":"Mingai","middleName":"","lastName":"Li","suffix":""},{"id":500482299,"identity":"2a94cd26-848c-4a2e-9a60-d4937dce5a7e","order_by":7,"name":"Claudio Varotto","email":"","orcid":"","institution":"Fondazione Edmund Mach","correspondingAuthor":false,"prefix":"","firstName":"Claudio","middleName":"","lastName":"Varotto","suffix":""},{"id":500482300,"identity":"509a00c5-189f-47f3-91ee-f9b7069f0a06","order_by":8,"name":"Hongjian Zheng","email":"","orcid":"","institution":"Shanghai Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hongjian","middleName":"","lastName":"Zheng","suffix":""},{"id":500482301,"identity":"7c7270e6-5509-4356-81d9-f68f09ba8fa6","order_by":9,"name":"Sang Yeol Lee","email":"","orcid":"","institution":"Gyeongsang National University","correspondingAuthor":false,"prefix":"","firstName":"Sang","middleName":"Yeol","lastName":"Lee","suffix":""},{"id":500482303,"identity":"aaf0e24e-cb4b-4fc3-9e49-6b9d63870880","order_by":10,"name":"Xin Liu","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Liu","suffix":""},{"id":500482306,"identity":"92898879-f044-416d-a28a-ce146637578d","order_by":11,"name":"Xiyun Song","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xiyun","middleName":"","lastName":"Song","suffix":""},{"id":500482307,"identity":"e32128be-d14b-4178-8b39-1508b884e14d","order_by":12,"name":"Meiai Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYFACxgaGhAoIU4IELWdI0wLS1UaKFt325sYPD+dZyxscYD54m4fBLo+gFrMzB5slErelG244wJZszcOQXExYy43EBqCWw4wbDvCYSfMwHEhsIKjl/sPmH4lzDttvOMD/jUgtNxjbJBIbDicCbWEjUsuZxDaLhGPpyTMPsxlbzjFIJkLL8eOPb/6osbbtO9788MabCjvCWqCAGYwYGAyIVM8AVT8KRsEoGAWjADsAALxkPk6+5+48AAAAAElFTkSuQmCC","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Meiai","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-07-30 17:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7255302/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7255302/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89510194,"identity":"bcc4bb20-52d9-4f80-9b60-e40a359dfc21","added_by":"auto","created_at":"2025-08-20 18:10:03","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":292189,"visible":true,"origin":"","legend":"\u003cp\u003eSectional histograms of the resistant and susceptible materials. (A): Resistant material QM1; (B): sensitive material HZ4. The red double arrows indicate the thickness of the cortex. (C): Schematic diagram of field inoculation. (D): Schematic diagram of disease resistance levels.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7255302/v1/2a8a4204ddc10aad8b4a9fe1.jpg"},{"id":89510196,"identity":"0131b968-cb9b-47be-885f-6a65051c8b25","added_by":"auto","created_at":"2025-08-20 18:10:03","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":343256,"visible":true,"origin":"","legend":"\u003cp\u003eHistogram of the distribution of fiber component data for 153 maize inbred lines. (A): 2020 lignin, (B): 2020 cellulose, (C): 2020 hemicellulose, (D): 2020 lignin, (E): 2020 cellulose, (F): 2020 hemicellulose, (G): BLUE lignin, (H): BLUE cellulose, (I): BLUE hemicellulose.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7255302/v1/1ec59b23149a34c581a03a34.jpg"},{"id":89510722,"identity":"f34ff251-bba5-45cf-97d0-17aef71d1e13","added_by":"auto","created_at":"2025-08-20 18:18:03","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":102791,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of stalk-related fiber components. Significance levels are indicated with * and ** for \u003cem\u003ep \u0026lt; 0.05\u003c/em\u003e and \u003cem\u003ep \u0026lt; 0.01\u003c/em\u003e, respectively.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7255302/v1/05bc950df44f70d6b1f12168.jpg"},{"id":89510723,"identity":"02aba1ca-d6dc-4862-9b88-762b5fc5e8c2","added_by":"auto","created_at":"2025-08-20 18:18:03","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":209695,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plots (Left) and Q‒Q (Right) plots of ADLs in Laiyang. GWAS results: (A) FarmCPU (B) BLINK software and (C) RTM-GWAS in 2020. Each dot in the plot represents a single SNP, where the x-axis denotes the genomic location, with chromosomes colored and labeled accordingly. The y-axis represents the association level, measured as −log\u003csup\u003e10\u003c/sup\u003e(p). The line (−log\u003csup\u003e10\u003c/sup\u003e(p) = 3) corresponds to a significance threshold of \u003cem\u003eP \u0026lt; 0.03\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7255302/v1/16405f80927a338cb4ebaf6a.jpg"},{"id":89510200,"identity":"64ede623-2a26-45f6-8dea-50d4b4cd91f0","added_by":"auto","created_at":"2025-08-20 18:10:03","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":519184,"visible":true,"origin":"","legend":"\u003cp\u003eThe localization of stalk fiber traits was accomplished by employing the BSA-seq technique in conjunction with indel molecular markers. (A): Distribution of SNP index values for the BR-bulk; (B): distribution of SNP index values for the BQ-bulk; (C): distribution of ∆SNP index values. The horizontal coordinates are the chromosome names, the colored dots represent the calculated SNP index (or ΔSNP index) values, and the black lines are the fitted SNP index (or ΔSNP index) values, where the red line represents the threshold line with a confidence level of 0.99, the blue line represents the threshold line with a confidence level of 0.95, and the green line represents the threshold line with a confidence level of 0.90. (D): Linkage map with Indel markers on chromosome 5; (E): QTL mapping for resistance to maize stalk fiber lignin.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7255302/v1/8ebb93a9daa36c7a6e4aa851.jpg"},{"id":89511411,"identity":"c2c80719-f55e-4eee-b725-3e4f6cfe24d1","added_by":"auto","created_at":"2025-08-20 18:26:04","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":223377,"visible":true,"origin":"","legend":"\u003cp\u003eAmong the four maize materials, the two candidate genes presented different expression patterns after treatment with \u003cem\u003eF. graminearum\u003c/em\u003e. (A): Expression of \u003cem\u003eLOC100276302\u003c/em\u003e; (B): expression of \u003cem\u003eLOC542735\u003c/em\u003e. The ratio of expression levels was calculated via the following formula: R=2^\u003csup\u003e- CT (target) – CT (actin-1)\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7255302/v1/e2340842d119ab98a801cd27.jpg"},{"id":89512292,"identity":"b1299681-a62d-40fe-9c12-70ac36f7f868","added_by":"auto","created_at":"2025-08-20 18:42:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2753605,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7255302/v1/57284070-d177-436c-913b-d46261cf4338.pdf"},{"id":89510725,"identity":"b996fe46-720f-467d-a21a-06a73f16326d","added_by":"auto","created_at":"2025-08-20 18:18:03","extension":"pptx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":235045,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1FigureS1.pptx","url":"https://assets-eu.researchsquare.com/files/rs-7255302/v1/0c86fa3bf3104cba456a773f.pptx"},{"id":89510216,"identity":"182e3c3d-0767-41ff-8e5a-343cc7a31887","added_by":"auto","created_at":"2025-08-20 18:10:04","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11789238,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2FigureS2.pptx","url":"https://assets-eu.researchsquare.com/files/rs-7255302/v1/41d714c99cdf7d8653806953.pptx"},{"id":89510224,"identity":"a946eb9b-8b4f-4230-8bad-cbf3ef654642","added_by":"auto","created_at":"2025-08-20 18:10:04","extension":"pptx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":12754424,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3FigureS3.pptx","url":"https://assets-eu.researchsquare.com/files/rs-7255302/v1/a5647ce1a7151dac4eeca4e4.pptx"},{"id":89510197,"identity":"68ed6cb8-fb95-470b-903f-5f933f562855","added_by":"auto","created_at":"2025-08-20 18:10:03","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":9356,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile5TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7255302/v1/5281359f0acddb584386e32c.xlsx"},{"id":89510236,"identity":"a361e30b-4971-4170-9f8d-e3000aa52a91","added_by":"auto","created_at":"2025-08-20 18:10:05","extension":"pptx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":10476201,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile4FigureS4.pptx","url":"https://assets-eu.researchsquare.com/files/rs-7255302/v1/c24f24b8bd56a0a56e6c6b58.pptx"},{"id":89510215,"identity":"e2c64010-df99-48f2-88af-98b2e89d747e","added_by":"auto","created_at":"2025-08-20 18:10:04","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":11501,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile6TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7255302/v1/d83fb90ba7df21b2c75487f6.xlsx"},{"id":89510223,"identity":"bf9c7834-fa33-4b3d-933c-a6ff2d6f966e","added_by":"auto","created_at":"2025-08-20 18:10:04","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":16389,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7255302/v1/60aa0cf185b057d2f1b53ff4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combination Genome-wide Association Study and Bulked Segregant Analysis Reveals the Loci Controlling Stalk Fiber Traits Related to Stalk Rot in Maize","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMaize (\u003cem\u003eZea mays L.\u003c/em\u003e) is a globally significant food crop that is crucial for ensuring food security [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. With the development of agriculture, plant diseases have become increasingly important factors affecting crop yield and economic efficiency. Among several disease-causing pests, \u003cem\u003eFusarium spp\u003c/em\u003e. Fungi and the diseases they cause pose the greatest threat to seed yield and quality[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Stalk rot is one of the most devastating soil-borne diseases in maize. The increasing incidence of this disease, coupled with the effects of climate change, has led to continued yield limitations in maize cultivation. Early field practice has shown that the stalk structure is closely related to stalk rot; therefore, studying the genetics of this association is imperative for enhancing resistance to this detrimental disease.\u003c/p\u003e\u003cp\u003eThe occurrence of maize stalk rot is closely related to the structure of the maize stalk. The plant cell wall serves as the primary defense against pathogen invasion, while the fibrous component of the stalk enhances the mechanical integrity of the secondary cell wall, thereby conferring resistance to stalk rot[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Moreover, the strength of the maize stalk is related to the cellulose and lignin contents of the relevant components of the stalk[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Lignin is an important component of the cell wall of vascular plants, imparting strength and rigidity to the cell wall and increasing the stiffness and mechanical strength of the plant body by filling gaps between cellulose structures[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. As a crucial factor, lignin content influences plant resistance to pathogen infection and enhances disease resistance[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The lignin content of highly resistant maize varieties is consistently greater than that of moderately resistant varieties, both before and after inoculation with \u003cem\u003eFusarium\u003c/em\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, the lignin content tends to increase during plant-induced disease resistance, which is consistent with elevated phenylalanine lyase and peroxidase activity, indicating the significant role of increased lignin content in plant disease resistance[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn addition, studies have been conducted to elucidate the genetic basis of stalk rot resistance in detail and to identify several quantitative trait loci (QTLs) and genes for resistance traits[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Genome-wide association studies (GWASs) have been widely used for functional gene discovery and have yielded many associations between markers and various complex traits[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Compared with traditional linkage mapping, these methods offer the advantages of reducing study time and increasing the number of detected alleles[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]; in maize, this approach has been effectively used to detect many QTLs or genomic regions that confer resistance to important maize diseases[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. For example, Oluk et al. (2016) conducted a GWAS using an association population comprising 274 maize inbred lines and successfully identified three loci associated with rust resistance in maize[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Ade et al. (2015) localized a sorghum stalk rot resistance-associated locus on chromosome 9 via the GWAS method[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Jing et al. (2021) performed a GWAS using the genome-wide distribution of 22,048 SNPs in soybeans and discovered that 18 SNPs associated with Sclerotinia stalk rot (SSR) resistance were distributed across 12 different soybean chromosomes[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Advancements in sequencing technology have facilitated the integration of BSA-seq technology with GWAS as a highly regarded and powerful tool for localizing QTLs. Bulk segregation analysis (BSA) is primarily applied to control quality trait loci but can also be utilized for quantitative traits controlled by main effect loci [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The BSA effectively controls false positives and enhances the accuracy of GWAS results. For example, Gyawali et al. (2019) obtained three significant SNP loci by combining a GWAS and BSA for maize height via 5000 pi269743 maize populations[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, in the present study, we used a combination of BSA and GWAS for the correlation analysis of lignin QTLs. The occurrence of maize stalk rot is closely related to the structure of maize fibers. When invaded by pathogens, the plant cell wall is used as the main line of defense against pathogen infestation and influences the strength of the stalk by affecting the tissue structure of the cell wall, which has a certain effect on the occurrence of stalk rot. The cellulose synthase gene family plays an important role in regulating secondary cell wall formation, with \u003cem\u003eZmCesA10\u003c/em\u003e, \u003cem\u003eZmCesA11\u003c/em\u003e, and \u003cem\u003eZmCesA12\u003c/em\u003e interacting with each other[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. An RIL population of 200 plants was constructed via the maize inbred lines B73 and By804, five loci controlling lignin were located on chromosomes 1, 2, 6, and 7, and three cellulose-related loci were also identified on chromosomes 1 and 6[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Similarly, in a population of 163 RILs constructed by Rio and WM13, four loci that are important for regulating lignin content were distributed on chromosomes 1, 2, 4, and 8, and a total of five loci related to cellulose content were identified in bins 1.07, 1.11, 3.04, 4.04, and 8.02. In addition, with respect to hemicellulose content, related loci were present at one location in bin 1.07, bin 1.11, bin 4.04, and bin 8.02[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe relationship between the stalk fiber fraction and maize rot has not previously been fully characterized. Although we have obtained some mapping results and genetic markers for stalk fiber fractions and maize resistance, there is still a need to explore in depth the genetic patterns of stalk fiber composition and its association with stalk rot. These findings are highly important for improving the disease resistance of crops and promoting agricultural production. In this study, 153 inbred maize lines from 2019 and 2020 were used as materials, and phenotypic analysis was carried out via inoculation of stalk rot pathogens in the field. The regulatory sites of stalk fiber components associated with maize stalk rot were subsequently explored via GWAS and BSA. Finally, we identified candidate genes regulating lignin content, which provides theoretical support for further exploration of the relationships between stalk rot and stalk components, analysis of the genetic rules and related genes of stalk fiber components, and study of stalk rot resistance in maize.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003ePlant materials and field management\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 153 maize inbred lines derived from four subgroups were analyzed in this study: China LvDahonggu, TangSiPingtou, Tropical Species Group, P Species Group, and sweet glutinous maize. The plant material and phenotypic analyses were consistent with those used in previous methods[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. We obtained 200 F2 individuals via recombinant selection using the disease-resistant line QM1 and the disease-susceptible line Huangzao4 (HZ4) as parents. The resulting recombinant F\u003csub\u003e2:3\u003c/sub\u003e (♀QM1 \u0026times; ♂HZ4) families obtained through self-pollination were genotyped for further analysis. These selfed lines were either collected or bred by the maize molecular breeding team at Qingdao Agricultural University.\u003c/p\u003e\u003cp\u003eIn this study, the association materials were planted in 2019 and 2020, whereas a BSA population was planted in 2021 in the Laiyang test field in China. The maize inbred line culture was conducted via a completely randomized block design with three replications at each experimental site. Each row consisted of 20 maize plants, with 3 m spacing between rows and 0.6 m spacing within rows. All the field management practices adhered to standard agricultural protocols.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInoculation and phenotyping\u003c/b\u003e\u003c/p\u003e\u003cp\u003eArtificial inoculation was performed via the injection method in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-C). \u003cem\u003eFusarium graminearum\u003c/em\u003e was cultured in Petri dishes containing potato dextrose agar medium at 26℃ in a dark chamber. One week before inoculation, the mung beans were boiled in water for 10 min and filtered through gauze. Then, the medium was transferred into flasks and autoclaved at 121℃ for 30 min. Five to six clusters approximately 1.5 ㎝ in diameter were used to inoculate 100 ml of mung bean medium, which was rotated at 180 rpm, continuously oscillated for 5‒7 days, and incubated at 28℃ until spores were produced from the solution. The spores were filtered and prepared for field inoculation. When the maize reached the staminate stage, we injected 1 ml of pathogen mixture into the second node of the above-ground stalk via a sterile syringe to ensure a slow and uniform inoculation rate to prevent spillage of the pathogen[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. After injection, the inoculation site was sealed with a breathable adhesive to prevent contamination by other pathogens and rain[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe scored plants that exhibited typical stalk rot symptoms at the milk-ripe stage. We longitudinally split the stalks at the upper and lower internodes and then observed the phenomenon after the plants were inoculated with the pathogen. The degree of infection in each stalk was classified on a scale of 1\u0026ndash;6 on the basis of the extent of browning in its internal tissues and the size of the decayed area (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-D). Materials of grades 1\u0026ndash;3 exhibit resistance, whereas those of grades 4\u0026ndash;6 display sensitivity[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A cross section of a stalk with a score of 6 exhibited complete decay and a deep brown color (100%), a score of 5 represented 80% decay and a brown color, a score of 4 represented a 50% brown color, a score of 3 represented a 30༅ brown area around the inoculation site, a score of 2 represented a 10༅ brown area, and a score of 1 represented almost no symptoms of rot. On the basis of Chiang et al.\u0026rsquo;s work, the grading values were converted to \u003cem\u003edisease severity index (DSI) values\u003c/em\u003e, which were calculated as \u003cem\u003eDSI\u003c/em\u003e (%)= [sum (class frequency\u0026times;score of rating class)]/[(total number of plants)\u0026times;(maximal disease index)]\u0026times;100[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. To increase the accuracy of the results by mitigating the influence of environmental and genetic effects, we employed the lme4 package in R software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org\u003c/span\u003e\u003cspan address=\"https://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to fit the best linear unbiased estimates (BLUE) value.\u003c/p\u003e\u003cp\u003eThe coefficients of kurtosis, skewness, and coefficient of variation (CV) for each trait were calculated via SPSS software and Excel 2019 and recorded in a table. The CV was calculated via the following formula: CV=(SD\u0026thinsp;\u0026divide;\u0026thinsp;Mean\u0026times;100%), where SD is the standard deviation. To generate a normal distribution map, the phenotypic data were subjected to normality testing analysis (Kolmogorov\u0026ndash;Smirnov method) and distribution-fitting analysis via Origin mapping software.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMicroscopic observation of the microstructure of the stalks\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo examine the microstructure of the stalks, samples were collected from the second internode at the stalk base. These samples were subsequently precisely sectioned into 1.0 cm \u0026times; 0.2 cm squares via a sharp blade and then immersed in FAA fixative for fixation. After fixation, the slices were sectioned via a paraffin slicer (Leica Microsystems Trading Co.). The wax blocks were stabilized and repositioned to a thickness of 8 \u0026micro;m. After sectioning, the wax slices were gently placed in ultrapure water preheated to 42\u0026deg;C for unfolding and retrieval via adsorbent slides. They were then placed on a dryer at 37\u0026deg;C for drying and set aside. Next, after the material was rehydrated, it was stained with 1% TBO (Solarbio) and visualized via a positive fluorescence microscope (Leica Microsystems CMS GmbH).\u003c/p\u003e\u003cp\u003e\u003cb\u003eDetermination of fiber composition\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter the disease resistance of the maize in the field was assessed, the recovered samples were placed in an oven for processing. The stalks were sterilized at 105\u0026deg;C for 30 min and then dried continuously at 65\u0026deg;C before being passed through a 100-mesh sieve after pulverization via a universal grinder. Spectral analysis of the cellulose, hemicellulose, and lignin in the maize stalk was performed via Fourier transform near-infrared spectroscopy. For NIR analysis, samples with less than 10% moisture that were dry and well mixed were used and scanned in a cup with a diameter of 40 mm. Each scan collected an average of three spectra to minimize sample heterogeneity error. The collected spectra were within the range of 4000\u0026thinsp;~\u0026thinsp;10000 nm. Additionally, the measured sample spectra were converted into numerical data via the proposed NIR spectral conversion model.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSequencing and genotype analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo identify resistance genes in tropical maize, a new association panel containing 153 individuals was constructed for disease analysis from the 384 panel (55k)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and tropical germplasm (20K)[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. By integrating two kinds of chips in 153 lines, 5585 markers were obtained. Quality checking (QC) was conducted with the method used in our previous study[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A total of 4,666 markers remained after QC. These markers contained multiple mutation types (more than 2,600 markers) in the allele, such as \u003cem\u003ePZE-108075114\u003c/em\u003e (chromosome 8, position 129390898), which included 9 mutation types: CC, TT, AA, GG, CT, AG, GT, CA, and CG. Here, we used GAPIT's new function, GAPIT HMP amplification, to code all the mutations as individual numeric values (1 indicates that the genotype exists, and 0 indicates that it does not exist). This method is similar to Johannes\u0026rsquo;s report in 2017 (Genomic prediction with epistasis models: on the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE)). Finally, 10,100 new markers were used for downstream analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGWAS Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe genotype data of the same population were quality checked in our previous study, and principal component analysis (PCA) was conducted with the PCA package in GAPIT[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. A plot of the first two principal components (PCs) was created to visualize the possible population stratification among the samples (Supplementary Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The P value was set as 1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e for the genome-wide significance threshold (P\u0026thinsp;=\u0026thinsp;1/n; n is the effective number of SNPs). To determine the best model for the component analysis of maize disease, three software programs (FarmCPU[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], BLINK[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and RTM-GWAS[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]) were used for GWAS analysis. Fixed and random model circulating probability uniform (FarmCPU) and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK) analyses were conducted with an R package (GAPIT), and the significant markers and P value distributions were visualized via Manhattan plots and quantile\u0026ndash;quantile plots (Q‒Q plots).\u003c/p\u003e\u003cp\u003e\u003cb\u003eLinkage mapping by bulked segregant analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOn the basis of the lignin content of the F\u003csub\u003e2:3\u003c/sub\u003e population, we selected 25 individuals with the highest and lowest lignin contents for screening. DNA was extracted separately via a high-throughput CTAB method, and equal amounts of each DNA sample were mixed into strong and weak pools. These samples were then sent to Beijing Baike Biotechnology Co. for sequencing. Additionally, the DNA of four samples (QM1, HZ4, the strong pool, and the weak pool) was randomly fragmented into 350 bp fragments via ultrasonication. The DNA from four samples (QM1, HZ4, strong pool, and weak pool) was randomly fragmented into 350 bp fragments via ultrasound technology. The fragments subsequently underwent end repair and nucleotide (A) overhang addition. These modified fragments were then ligated to the sequencing adapter via T4 DNA ligase and subsequently amplified via PCR.\u003c/p\u003e\u003cp\u003eAfter the construction of the libraries, the DNA from the extremely mixed pools in the F\u003csub\u003e2:3\u003c/sub\u003e family line was sequenced. Clean reads were aligned to the maize reference genome (B73 RefGen v3) via Burrows\u0026ndash;Wheeler Aligner software, with a read length depth of less than 6 and an SNP index of less than 3. All indices for SNP/indel/SV and difference values for the two extreme pools were calculated[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. To determine the physical location of each SNP, analysis of variance (ANNOVAR) was used for SNP location annotation. Intervals exceeding the threshold at the 95% confidence level were subsequently selected on the basis of ∆ (SNP index). The InDel (insertion and deletion)/SNP site sequences were subsequently extracted from the resequencing data for molecular marker development.\u003c/p\u003e\u003cp\u003eThe PCR products were separated via 8% denaturing polyacrylamide gel electrophoresis, followed by silver staining, and genotype data were collected accordingly. Primer 5 software was employed to design insertion\u0026ndash;deletion markers on the basis of the QM1 and HZ4 resequencing data. Subsequently, individuals exhibiting recessive traits were chosen from the QM1 \u0026times; HZ4 F\u003csub\u003e2:3\u003c/sub\u003e population planted in 2020 for genotyping purposes. Finally, Ici mapping software was utilized to construct a chain map with an LOD threshold set at 2.5. Linkage mapping of a specific site was conducted to verify the loci from the BSA[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eSNP annotation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCandidate genes associated with target traits were identified via the MaizeGDB database (B73_RefGen_v3) on the basis of the physical positions of significantly associated SNPs. The molecular marker database in MaizeGDB was utilized to examine genes corresponding to each SNP locus, and functional annotations of candidate genes were predicted through NCBI.\u003c/p\u003e\u003cp\u003e\u003cb\u003eQuantitative real-time PCR (qRT\u0026ndash;PCR)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe expression patterns of candidate resistance genes in various maize cultivars under \u003cem\u003eFusarium graminearum\u003c/em\u003e infestation were analyzed via qRT‒PCR. Samples were collected at 0, 24, 36, and 48 h post inoculation and immediately frozen in liquid nitrogen for subsequent experiments. An RNA Extraction Kit (TAKARA) was used for RNA extraction, followed by reverse transcription into cDNA. The primers used were as follows: \u003cem\u003eActin-1\u003c/em\u003e (\u003cem\u003eGRMZM2G126010\u003c/em\u003e) F: 5'-GTCCATGAGGCCACGTACAA-3', R: 5'-CCGGACCAGTTTCGTCATA-3'; \u003cem\u003ePZE-105083600\u003c/em\u003e F: 5'-AGCTCGTGAGAAGCAAACCT-3', R: 5'-CATTTGACCTTGGGACACGA-3'; \u003cem\u003ePZE-107034235\u003c/em\u003e F: 5'-GAAGTGCTGCATGATATGTGGG-3', R: 5'-GCTGCGAGTCGAACCTT \u0026minus;\u0026thinsp;3'. The qRT‒PCRs were conducted on a T100TM thermocycler (Bio-Rad) with a 2\u0026times; ChamQ Universal SYBR qPCR Master Mix Kit (Vazyme). The cycling conditions included initial denaturation at 94℃ for 30 s, followed by amplification over the course of 40 cycles of denaturation at 95℃ for 10 s, annealing at 55℃ for 10 s, and extension at 72℃ for 30 s. \u003cem\u003eActin-1\u003c/em\u003e served as an internal reference gene in the qRT‒PCR analysis. Experiments were performed on disease-resistant and disease-sensitive materials and independently repeated at least three times to ensure test accuracy. The ratio of expression levels was calculated via the following formula: R\u0026thinsp;=\u0026thinsp;2^\u003csup\u003e\u0026minus; CT (target) \u0026ndash; CT (\u003cem\u003eactin\u003c/em\u003e\u0026minus;1)\u003c/sup\u003e [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003ePhenotype Statistics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study, 153 samples from the Reid, LvDahonggu, group P, and TangSiPingtou subgroups, as well as some tropical lines and sweet Ceres inbred lines, were analyzed.\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, we analyzed the continuous change trends in the composition of stalk fibers associated with stalk rot, revealing a wide range of variations in lignin, cellulose, and hemicellulose contents. The lignin content varied from 3.07\u0026ndash;43.22%, while the cellulose content ranged from 7.07\u0026ndash;40.40%. Except for the slightly skewed BLUE value for lignin (0.849), the absolute skewness values for all the other data were less than 1, indicating stable distributions with balanced kurtosis values (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-I). These results suggested that most of the trait distributions among the inbred maize lines presented normal distribution patterns suitable for QTL mapping. After comprehensive analysis of the data, it was determined that most of the traits in these inbred lines exhibited a stable distribution and adhered to customary distribution laws, indicating their reliability and stability. However, certain traits presented significantly expressed eigenvalues, which may be associated with the genotype of the maize self-inbred lines. Therefore, these prominently expressed eigenvalues can be further investigated to better understand the genetic factors underlying them.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePhenotypic identification of stalk-associated fiber components.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSD*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCV*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSkewness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eKurtosis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eADL*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBLUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e40.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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colname=\"c9\"\u003e\u003cp\u003e-0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e35.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e25.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBLUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e28.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e22.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHCEL*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e13.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBLUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eTraits. ADL: lignin; CEL: cellulose; HCEL: hemicellulose.; SD, standard deviation; CV, coefficient of variation. CV=(SD\u0026thinsp;\u0026divide;\u0026thinsp;Mean\u0026times;100%)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelation analysis of related fiber components in the stalks\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo ensure the accuracy and consistency of our findings, we conducted a two-year analysis of significant correlations among three fiber compositions related to maize stalks via SPSS 20.0 software. The results, presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, revealed significant correlations between \u003cem\u003eDSI\u003c/em\u003e and ADL and between CEL and HCEL, as well as highly significant correlations between ADL, CEL, and HCEL in self-fertilized line populations during 2019 and 2020. Additionally, \u003cem\u003eDSI\u003c/em\u003e exhibited a highly significant negative correlation with ADL in both years (correlation coefficients of -0.219 and \u0026minus;\u0026thinsp;0.400; \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e), indicating the crucial role of ADL in maize stalk rot. Conversely, the CEL and HCEL were positively correlated with the \u003cem\u003eDSI\u003c/em\u003e, suggesting a trade-off relationship between these indicators. In general, higher levels of ADL and lower levels of CEL correspond to increased disease resistance. We found a significant correlation between \u003cem\u003eDSI\u003c/em\u003e and the three components of SAFs, indicating that regulating SAFs is necessary for effective control of disease resistance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMicrostructure analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo explore the differences in stalk tissue structure between resistant (QM1) and susceptible (HZ4) lines, paraffin sections were obtained from the central region of the second basal internode of the above-ground segment at maturity and examined from a cross-sectional perspective (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). The results revealed that the area of vascular bundles in QM1 was significantly greater than that in HZ4, whereas the number of vascular bundles in QM1 was lower. Additionally, the distribution of vascular bundles in QM1 was more uniform and orderly than that in HZ4. Furthermore, the cell wall thickness of QM1 was significantly greater than that of HZ4. The histological structures of the stalks differed significantly between the resistant and sensitive materials. These findings indicate substantial histological structural disparities between resistant and susceptible materials, which might influence stalk rot development through their effects on stalk strength.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGWAS analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo assess SNP locus correlations between maize stalk rot and stalk fiber components (ADL, CEL, and HCEL), we used three different GWAS analysis models and performed a two-year GWAS of three types of trait data in three approaches (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Supplementary Figures \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u0026ndash;S4). The quantity of each genotype was checked in our previous study[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]; the minor allele frequencies of 4658 markers were at least 5%, and the fitted model revealed that the LD decayed to 0.1 at 300 Mb[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. On the basis of the PCA results, the panel of accessions was clustered into four groups; they exhibited distinct separation along the first principal component (PC1), the second principal component (PC2), and the third principal component (PC3) axes (Supplementary Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). To identify key genes associated with stalk fiber composition, the significance threshold was set at 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e. The quantile‒quantile (Q‒Q) plots obtained via the three methods revealed that the population structure and correlation between traits in the association groups were well controlled. Through comprehensive analysis via three GWAS packages (FarmCPU, BLINK, and RTM-GWAS), a total of 192 SNP loci significantly associated with stalk fiber component traits were identified across diverse analysis models. These SNPs correlated with ADL (77), CEL (62), and HCEL (53). FarmCPU analysis revealed a total of 33 SNPs, whereas BLINK presented the highest detection rate, with 116 SNPs. The RTM-GWAS approach identified 43 SNPs. Upon comparing the results obtained from different models, we consistently identified 20 SNPs via two or more analytical methods (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among these markers, twelve SNPs loci located on chromosomes 1, 2, 3, 5, 6, 7, 9, and 10 were associated with ADL; another set of twelve SNPs loci situated on chromosomes 1, 2, 3, 5, 6, 7, and 10 showed associations with the CEL content. Additionally, thirteen HCEL-associated SNPs loci were discovered on chromosomes 1, 2, 3, 4, 5, 8, 9, and 10. Notably, the two most stable SNPs (\u003cem\u003ePZE-101211038\u003c/em\u003e and \u003cem\u003ePZE-105021925\u003c/em\u003e) were consistently detected across most environments by all three models, and these two loci were significantly correlated with the ADL, CEL, and HCEL traits. This approach, which employs a combination of multiple GWAS models, enables enhanced screening capabilities, resulting in improved accuracy of our findings.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSNPs associated with resistance to stalk fiber traits related to stalk rot in maize according to the four models.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSNP\u0026nbsp;maker\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChr\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePosition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePhenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMethod\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePZE-103093664\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e150818560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eADL、CEL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eB、F\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePZE-105048147\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37578032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eADL、CEL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eB、F\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePZE-105083600\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99732524\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eADL、CEL、HCEL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eB、F\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePZE-106053973\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e104827794\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eADL、CEL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eB、F\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePZE-102091357\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95313459\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eADL、CEL、HCEL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eB、F\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePZE-110009225\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6688491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eADL、CEL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eB、F\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePZE-110013761\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12806853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eADL、CEL、HCEL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eB、F\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePZE-110047844\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89464132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eADL、CEL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eB、F\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSYN15334\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3864123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHCEL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eB、F\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePZE-104025517\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30059447\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHCEL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eB、R\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePZE-101247830\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e292211608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCEL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF、R\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePZE-104099090\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e174587871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHCEL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF、R\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSYN13541\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45863137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHCEL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF、R\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePZE-108067299\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e117640295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHCEL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF、R\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePZE-107121188\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e160869730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eADL、CEL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eB、F、R\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePZE-101178823\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e223152986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHCEL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eB、F、R\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSYN5650\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4641951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHCEL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eB、F、R\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePZE-105021925\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10393432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eADL、CEL、HCEL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eB、F、R\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePZE-109090062\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e133448843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eADL、HCEL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eB、F、R\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePZE-101211038\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e259863931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eADL、CEL、HCEL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eB、F、R\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eChr, chromosome; F; FarmCPU; B: BLINK; R: RTM-GWAS\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eBSA-seq analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo validate the candidate regions obtained from the GWAS, an integrated BSA and resequencing approach was used. Fifty plants with extreme F\u003csub\u003e2:3\u003c/sub\u003e phenotypes from the cross QM1 \u0026times; HZ4 were selected, and their DNA was pooled together to construct two DNA bulks: the BR-bulk and the BQ-bulk (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The total amount of clean data generated was 279.07 G, with an average ratio of 92.03% and an average GC content of 46.86% (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). After screening, a total of 35,999 SNPs and 535,954 InDels were identified compared with the reference genome MaizeGDB database (B73_RefGen_v3). The offspring SNP frequency differences were subsequently integrated to calculate the SNP index ∆ (SNP index) for the pooled populations of BR-bulk and BQ-bulk. Differences in SNP indices (ΔSNP indices) were determined via a sliding window size of 1000 kb with a step size of 10 kb across ten chromosomes. On the basis of the ΔSNP index values, two candidate intervals were identified: Chr 3, 177.8\u0026ndash;227.0 Mb, and Chr 5, 24.1\u0026ndash;154.7 Mb (Fig.\u0026nbsp;5).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(E)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLeft Marker\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRight Marker\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLOD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePVE (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAdd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDom\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e92131681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e116615470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-8.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-9.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e92131681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e116615470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-9.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e92131681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e116615470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-8.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-8.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e116615470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e116704059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-18.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure 5. The localization of stalk fiber traits was accomplished by employing the BSA-seq technique in conjunction with indel molecular markers. (A): Distribution of SNP index values for the BR-bulk; (B): distribution of SNP index values for the BQ-bulk; (C): distribution of ∆SNP index values. The horizontal coordinates are the chromosome names, the colored dots represent the calculated SNP index (or ΔSNP index) values, and the black lines are the fitted SNP index (or ΔSNP index) values, where the red line represents the threshold line with a confidence level of 0.99, the blue line represents the threshold line with a confidence level of 0.95, and the green line represents the threshold line with a confidence level of 0.90. (D): Linkage map with Indel markers on chromosome 5; (E): QTL mapping for resistance to maize stalk fiber lignin.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTwo potential candidate genes identified via GWAS and BSA\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further validate the QTL loci, InDel primers were developed on the basis of the resequencing data of chromosome 5 from 24.1\u0026ndash;154.7 Mb. Initially, we used 56 pairs of InDel marker primers to detect polymorphisms in the parental lines HZ4 and QM1 as well as in the F\u003csub\u003e2:3\u003c/sub\u003e population. Among these primers, only 13 produced definitive genotyping results for both parents and the F\u003csub\u003e2:3\u003c/sub\u003e generations. We genotyped 42 resistant individuals from the F\u003csub\u003e2:3\u003c/sub\u003e population via these 13 primer pairs (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Local linkage genetic maps were constructed via Ici mapping software to analyze the relationships between lignin content and genetic loci, which led us to identify two significant QTLs on chromosome 5 with LOD values ranging from 13.13 to 17.25 and contribution rates ranging from 15.78\u0026ndash;16.60%. When the LOD threshold was set to 2.5, four QTL regions were identified on chromosome 5, with LOD values ranging from 14\u0026ndash;18% (Fig.\u0026nbsp;5). Finally, BSA and GWAS screening methods identified a candidate gene, \u003cem\u003eLOC100276302\u003c/em\u003e (\u003cem\u003eGRMZM2G119890\u003c/em\u003e), on chromosome 5 with a \u003cem\u003ePZE105083600\u003c/em\u003e marker. Through GWAS screening, approximately 300 kb away on chromosome 7, the marker \u003cem\u003ePZE-107034235\u003c/em\u003e was identified, and the candidate gene was \u003cem\u003eLOC542735\u003c/em\u003e (\u003cem\u003eGRMZM2G131836\u003c/em\u003e), which is a \u003cem\u003eCinnamoy-CoA reductase\u003c/em\u003e (\u003cem\u003eCCR2\u003c/em\u003e) gene that plays an important role in the lignin synthesis pathway.\u003c/p\u003e\u003cp\u003eTo validate the association of candidate genes with lignin, we conducted fluorescence quantitative analysis on pivotal candidate genes identified through the GWAS and BSA. The \u003cem\u003eLOC100276302\u003c/em\u003e gene in the \u003cem\u003ePZE-105083600\u003c/em\u003e locus and the \u003cem\u003eLOC542735\u003c/em\u003e gene in the \u003cem\u003ePZE-107034235\u003c/em\u003e locus were detected via qRT‒PCR in different materials after inoculation with \u003cem\u003eF. graminearum\u003c/em\u003e, and the results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The results of the present study revealed that the \u003cem\u003eLOC100276302\u003c/em\u003e gene at the \u003cem\u003ePZE-105083600\u003c/em\u003e locus in QM1 and QM2, which are highly resistant to stalk rot, gradually increased over time and was significantly upregulated at 48 hr. The expression of the candidate gene was elevated approximately 2.25-fold and 1.8-fold in QM1 and QM2, respectively, compared with that at 0 hr. The expression of the \u003cem\u003eLOC542735\u003c/em\u003e gene increased significantly over time in the disease-resistant lines, by 1.85-fold and 1.79-fold at 48 h, respectively, which was much greater than that in the susceptible lines. On the basis of the above results, we inferred that the candidate genes \u003cem\u003eLOC100276302\u003c/em\u003e and \u003cem\u003eLOC542735\u003c/em\u003e were closely related to maize stalk rot production and fiber composition.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe structure and composition of maize stalks are intricately linked to the development of stalk rot. In this study, we employed a combination of GWAS and BSA-seq methodologies to identify genes associated with maize stalk rot-related fiber composition, which were further validated through fluorescence quantification. The results demonstrated the efficacy of this integrated approach in pinpointing relevant candidate genes. Additionally, the three GWAS methods successfully identified SNPs and novel loci that corroborated previous findings[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], thereby offering novel markers and genes for comprehensive investigations into maize stalks and providing theoretical support for enhancing disease resistance in maize varieties.\u003c/p\u003e\u003cp\u003eLignin, as a constituent of stalk fibers, may play a pivotal role in conferring resistance to maize stalk rot[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Microscopic observations of paraffin sections obtained from the second substrate internode of the resistant (QM1) and sensitive (HZ4) lines revealed significant histological differences between these two materials. Specifically, compared with HZ4, QM1 exhibited a significantly thicker cortex, which was characterized by fewer but larger vascular bundles, which is consistent with previous findings. We postulated that the disparity in disease resistance could be attributed to variations in fiber composition between these materials. The resistant line of QM1 might recognize fungal infection and initiate defense mechanisms to establish an impervious barrier against pathogen invasion. Upon the penetration of \u003cem\u003eFusarium graminearum\u003c/em\u003e through the cell membrane, cell wall reinforcement is rapidly activated [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], involving multiple genes responsible for increasing the lignin content and other constituents[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Lignin, one of the primary constituents of the cell wall, enhances the cellular structural barrier and confers protection against pathogens[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Furthermore, lignification, or the defense response, represents a crucial mechanism employed by plants to counteract pathogenic attacks. The promotion of lignification reinforces the mechanical strength of the cell wall while reducing its susceptibility to the degrading enzymes produced by Fusarium graminearum during the infection process and response to \u003cem\u003eFusarium\u003c/em\u003e head blight[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], as well as limiting the diffusion of pathogenic compounds[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] impeding pathogen invasion. These collective findings, in conjunction with our research outcomes, underscore the integral role played by lignin in mediating plant resistance against pathogens.\u003c/p\u003e\u003cp\u003eIn this study, we used three different pieces of analytical software to perform GWASs for data on cellulose, lignin, and hemicellulose contents measured over two years. A total of 192 SNP loci were detected, and 20 markers were detected via at least two methods. A total of 33 SNP loci closely related to maize stalk rot were detected via FarmCPU analysis. These SNP loci were distributed across the 10 chromosomes of maize. Specifically, a maximum of 6 loci were detected on chromosomes 1 and 5, whereas only 1 SNP locus was detected on chromosomes 4, 6, and 8, such as \u003cem\u003ePZE-101211038\u003c/em\u003e, \u003cem\u003ePZE-103093664\u003c/em\u003e, \u003cem\u003ePZE-105048147\u003c/em\u003e, and \u003cem\u003ePZE-106053973\u003c/em\u003e, which were also detected via BLINK. The marker \u003cem\u003ePZE-104025517\u003c/em\u003e was screened with both BLINK and RTM-GWAS. A total of 116 SNPs were identified with BLINK, and the maximum number of SNP loci detected on chromosome 3 was 19. Chromosome 7 had the second highest number (16), on which the \u003cem\u003ePZE-105021925\u003c/em\u003e marker was co-identified via two other methods. A total of 43 SNP loci were detected with RTM-GWAS, while chromosome 1 had the highest distribution. In summary, the maximum number of markers was detected on chromosome 1, and the fewest were detected on chromosome 6 with BLINK, FarmCPU, and RTM-GWAS together. This finding indicated that candidate genes for the target traits may be present on this chromosome. The number of ADLs detected in BLINK reached 46. A total of 19 sites were detected via RTM-GWAS, and at least 12 sites were detected via FarmCPU. This finding suggested that BLINK was more effective in detecting relevant sites than FarmCPU was. RTM-GWAS was able to detect potential SNPs more comprehensively, helping to avoid anything that might be missed. On chromosome 10, \u003cem\u003ePZE-110009225\u003c/em\u003e, which encodes an \u003cem\u003eLHW transcription factor\u003c/em\u003e (\u003cem\u003eGRMZM2G031656\u003c/em\u003e), was detected simultaneously by two models. Previous studies in \u003cem\u003eArabidopsis\u003c/em\u003e have demonstrated that \u003cem\u003eLHW transcription factors\u003c/em\u003e play pivotal roles in the differentiation and proliferation of xylem cells, with their activity being regulated by auxin signaling[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. A study by Jos R. Wendrich et al. (2020) also indicated that the \u003cem\u003eLHW/TMO5\u003c/em\u003e complex regulates the biosynthesis of cytokinin, thereby influencing the proliferation and division of vascular cells. Since the development of vascular tissues, including xylem, is closely related to the response of root hair cells, the \u003cem\u003eLHW/TMO5\u003c/em\u003e complex may have an impact on the development of xylem[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Moreover, \u003cem\u003ePZE-108067299\u003c/em\u003e on chromosome 8, which encodes \u003cem\u003eWRKY transcription factor 38\u003c/em\u003e (\u003cem\u003eGRMZM2G432583\u003c/em\u003e), was associated with HCEL traits and detected with two methods. Studies have shown that \u003cem\u003eWRKY transcription factors\u003c/em\u003e play crucial roles in plant defense responses against pathogens. For instance, \u003cem\u003eAtWRKY33\u003c/em\u003e has been demonstrated to be essential for resistance against necrotrophic fungal pathogens such as Botrytis cinerea, by regulating the expression of defense-related genes and phytoalexin biosynthesis[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Similarly, \u003cem\u003eOsWRKY13\u003c/em\u003e in rice mediates resistance to both \u003cem\u003eMagnaporthe oryzae\u003c/em\u003e and \u003cem\u003eXanthomonas oryzae pv. oryzae\u003c/em\u003e by modulating the expression of defense-related genes through salicylic acid and jasmonic acid signaling pathways[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In \u003cem\u003epepper\u003c/em\u003e, \u003cem\u003eCaWRKY58\u003c/em\u003e has been shown to negatively regulate resistance to \u003cem\u003eRalstonia solanacearum\u003c/em\u003e, highlighting the diverse roles of \u003cem\u003eWRKY transcription factors\u003c/em\u003e in different plant species [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. These findings underscore the importance of \u003cem\u003eWRKY transcription factors\u003c/em\u003e in mediating plant immune responses and suggest that \u003cem\u003eGRMZM2G432583\u003c/em\u003e, encoding \u003cem\u003eWRKY transcription factor 38\u003c/em\u003e, may similarly contribute to defense mechanisms in maize.\u003c/p\u003e\u003cp\u003eIn this study, we employed three GWAS software packages to identify SNP loci associated with the ADL, CEL, and HCEL components, revealing variations across different models. The results of these models revealed that the FarmCPU performed faster than did the other two packages while producing intermediate numbers of SNPs. This is primarily because the FarmCPU divides its model into two parts, i.e., a fixed effect model (FEM) and random effects model (REM), where the FEM includes one test marker at a time while utilizing other associated markers as covariates to control false positives; the REM optimizes these associated markers through affinity definitions and an optimization process. The computational time required for FEM-REM iterative approaches is linearly related to the sample size and number of markers[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Second, the BLINK software results in the greatest number of detection sites, demonstrating its superior detection capability and utilization. This is because each time a marker is detected, BLINK adds pseudo-QTN as a covariate to control for false positives and reduce false negatives; the more pseudo-QTNs there are, the greater the likelihood that a QTN will be detected[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In addition, BLINK and FarmCPU produce similar results. This is because FarmCPU and BLINK have the same level of performance. Additionally, BLINK detects more SNP loci than FarmCPU does. This is because BLINK is designed to reduce the computation time by replacing the REM with an FEM using the Bayesian information criterion (BIC) and determining the set of pseudo-QTNs that optimally control false alarms and minimize missed alarms. Thus, it compensates for the lack of FarmCPU[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In addition, compared with FarmCPU, RTM-GWAS detects a greater number of loci. This can be attributed to the two-stage analysis strategy implemented by RTM-GWAS, which effectively reduces the computational complexity of the multilocus model and enables the identification of more significant loci. Additionally, the markers utilized by this software facilitate the detection of polymorphic allelic variation in genome-wide QTLs, thereby enabling the identification of a greater number of SNP loci at a similar level of significance. He et al. also demonstrated that this method successfully identified more SNP loci across 1024 soybean species [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In this study, target trait-related SNPs were detected via the above three methods. We believe that an SNP detected via at least two methods is worthy of further investigation. A total of 20 SNP loci were detected within the two models and exhibited significant concordance with previous findings. Therefore, integrating the results from diverse GWAS models is imperative for obtaining more relevant SNPs[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. This methodology has been implemented in several prior studies [\u003cspan additionalcitationids=\"CR55 CR56\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].GWAS analysis is a crucial approach for identifying genotype\u0026ndash;phenotype associations, and its results are influenced by a variety of factors, such as phenotypic variation, the number of individuals, population structure, and allele frequency[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. During the assay, caution must be exercised to avoid false-positive or false-negative results. To increase the certainty of GWAS results, a combination of BSAs serves as a common complementary method to reduce uncertainty and improve accuracy. Previous studies have demonstrated that GWASs and the BSA can mutually complement each other in accurately identifying key regions. For example, four QTLs associated with maize prolificacy were jointly identified through a GWAS of 492 different maize inbred lines combined with a BSA of an F2 population consisting of the few-ear line Zheng58 and multiyear line 647[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Similarly, this combined approach was employed to validate the primary QTL associated with water tolerance in maize[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. In addition, three genomic regions related to maize plant height were identified as key QTLs for waterlogging tolerance via this technique[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In this study, we used a combination of GWAS and BSA to obtain lignin-associated gene intervals that were found to be consistent with the lignin-associated gene intervals identified by Guan et al. via an F\u003csub\u003e1\u003c/sub\u003e population generated from 400 maize hybrid combinations[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. These findings demonstrated that both GWAS and BSA are effective methods for efficiently identifying key genes for identifying stalk fiber components associated with maize stalk rot.\u003c/p\u003e\u003cp\u003eThe regulatory mechanisms underlying maize stalk rot remain incompletely understood. In this study, we employed a combination of GWAS and BSA techniques to pinpoint a candidate gene, \u003cem\u003eLOC100276302\u003c/em\u003e (\u003cem\u003eGRMZM2G119890\u003c/em\u003e), located on chromosome 5. The results of the quantitative fluorescence analysis revealed that the expression of the \u003cem\u003eLOC100276302\u003c/em\u003e gene located at the \u003cem\u003ePZE-105083600\u003c/em\u003e locus increased significantly with time in the disease-resistant lines and reached the highest level at 48 hr. However, it was only slightly upregulated in the susceptible lines. This finding indicated that the gene played a role when \u003cem\u003eFusarium graminearum\u003c/em\u003e attacked maize. The \u003cem\u003eLOC542735\u003c/em\u003e gene, located at locus \u003cem\u003ePZE-107034235\u003c/em\u003e, was expressed in disease-resistant materials at a similar level to \u003cem\u003ePZE-105083600\u003c/em\u003e, and the disease-resistant material presented 3.9-fold greater fluorescence than did the susceptible material. This suggested that the responses of this gene in disease-resistant and disease-susceptible materials were very different; \u003cem\u003eLOC542735\u003c/em\u003e might play an important role in attack by \u003cem\u003eF. graminearum\u003c/em\u003e. Although \u003cem\u003eLOC100276302\u003c/em\u003e was not clearly characterized by NCBI, our analyses revealed a number of features identical to those of the homologous gene, suggesting that \u003cem\u003eLOC100276302\u003c/em\u003e may be involved in cell wall peptide synthesis and relevantly associated with lignin biosynthesis, with functional similarity to its counterpart in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, \u003cem\u003eAT4G37090\u003c/em\u003e. Furthermore, the key \u003cem\u003eCCR2\u003c/em\u003e gene, \u003cem\u003eLOC542735\u003c/em\u003e on Chr7, was related to biosynthetic pathways in numerous studies. Notably, previous research involving CRISPR/Cas9-generated mutants lacking \u003cem\u003eCCR2\u003c/em\u003e has demonstrated differential effects on the lignin content and growth of \u003cem\u003eCCR2\u003c/em\u003e amino acids[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. In addition, a peroxidase-related QTL was detected on chromosome 7[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], and a \u003cem\u003eCCR\u003c/em\u003e gene was also identified, which is in agreement with our results. In summary, these two content-regulated candidate genes provide valuable insights for investigating the relationship between maize stalk fiber composition and resistance to stalk rot disease while guiding optimization strategies for enhancing stalk fiber fractions and disease resistance.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe present study revealed a significant correlation between maize stalk rot and the composition of stalk fibers. Microstructural analysis revealed that the disease-resistant inbred line QM1 presented a significantly thicker cortex than did the disease-susceptible inbred line HZ4, while it also presented fewer vascular bundles but larger bundle areas than did HZ4. By employing replicated screening methods, including FarmCPU, BLINK, and RTM-GWAS, a total of 20 markers were identified by at least two of these approaches. The integration of GWAS and BSA for stalk lignin content led to the identification of \u003cem\u003ePZE-105083600\u003c/em\u003e on Chr5 and \u003cem\u003ePZE-107034235\u003c/em\u003e on Chr7 as candidate genes regulating lignin content. The fluorescence quantification results revealed significantly higher expression levels of these two genes in disease-resistant materials than in disease-sensitive materials. This study is highly important for further exploration of the regulatory genes associated with maize stalk rot-related stalk fiber composition and the breeding of disease-resistant varieties.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGenome-Wide Association Study\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBSA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBulked Segregant Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eQTL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eQuantitative Trait Locus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSingle Nucleotide Polymorphism\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eInDel\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInsertion/Deletion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eADL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLignin (Alder)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCEL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCellulose\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHCEL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHemicellulose\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDSI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDisease Severity Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBLUE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBest Linear Unbiased Estimates\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrincipal Component Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFEM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFixed Effect Model\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eREM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRandom Effect Model\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFarmCPU\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFixed and Random Model Circulating Probability Unification\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBLINK\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBayesian-information and Linkage-disequilibrium Iteratively Nested Keyway\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRTM-GWAS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRandomized Tree Model for Genome-Wide Association Studies\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLinkage Disequilibrium\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLOD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLogarithm of Odds\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePVE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePercentage of Variance Explained\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAdd\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAdditive Effect\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDom\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDominance Effect\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eqRT\u0026ndash;PCR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eQuantitative Real-Time Polymerase Chain Reaction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNIR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNear-Infrared\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCoefficient of Variation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCTAB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCetyltrimethylammonium Bromide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTBO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eToluidine Blue O\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePolymerase Chain Reaction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eQ‒Q plot\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eQuantile\u0026ndash;Quantile Plot\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMANOVA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMultivariate Analysis of Variance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eANOVA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAnalysis of Variance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFalse Discovery Rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMAF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMinor Allele Frequency\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCycle Threshold\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBIC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBayesian Information Criterion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eQTN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eQuantitative Trait Nucleotide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGRMZM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGenetic Resource of Maize Zea mays\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCCR2\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCinnamoyl-CoA Reductase 2\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWRKY\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWRKY Transcription Factor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLHW\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLHW Transcription Factor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTMO5\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTMO5 Transcription Factor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch3\u003eConflict of Interest\u003c/h3\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch3\u003eEthics, Consent to Participate\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003eConsent to Publish declarations\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003eClinical trial number\u003c/h3\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e\n\u003ch3\u003eAuthor Contributions\u003c/h3\u003e\n\u003cp\u003eConceptualization: M-AZ , Y-L, X-W \u0026nbsp;and Y-HY; Data curation: Y-HY and Y-L; Funding acquisition: M-AZ, X-L and X-YSo; Investigation: M-AZ and Y-L; Methodology: Y-L and Z-GS; Project administration: Z-GS, Z-XL, S-YL, C-V, H-JZ and X-L; Validation: X-W, M-GL and C-V; Visualization: Y-HY, Z-XL, K-P Seol and H-JZ; Writing \u0026ndash; original draft: Y-HY and Z-GS; Writing \u0026ndash; review \u0026amp; editing: M-AZ, Y-L, X-W, S-YL, Z-XL, X-L and X-YSo.\u003c/p\u003e\n\u003ch3\u003eAcknowledgments\u003c/h3\u003e\n\u003cp\u003eWe thank the anonymous referees for their critical comments on this manuscript.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis research was supported by the National Natural Science Foundation of China (Grant No. 32372165), Shandong Province Seed Improvement Project (SDAIT-01-022-01), Shandong Province Key Research and Development Program (2016GNC110018), and the Academy of Dong Ying Efficient Agricultural Technology.\u003c/p\u003e\n\u003ch3\u003eAvailability of Data and Materials\u003c/h3\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on request.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhu XM, Shao XY, Pei YH, Guo XM, Li J, Song XY, et al. 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Identification of major QTL for waterlogging tolerance in maize using genome-wide association study and bulked sample analysis. J Appl Genet. 2021;62:405\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuan HH, Liu WS, Guo JJ, Zhao YF, Zhu LY, Huang YO, et al. Analysis of stalk fiber quality and combining ability in different maize heterotic groups. J Plant Genet Resour. 2018;19:925\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Meester B, Madariaga Calder\u0026oacute;n B, De Vries L, Pollier J, Goeminne G, Van Doorsselaere J, et al. Tailoring poplar lignin without yield penalty by combining a null and haploinsufficient CINNAMOYL-CoA REDUCTASE2 allele. Nat Commun. 2020;11:5020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWhetten R, Sederoff R. Lignin Biosynthesis. Plant Cell. 1995;7:1001\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Stalk fiber component, Genome-wide association study (GWAS), Bulked segregant analysis (BSA), lignin, BLINK, Maize stalk rot","lastPublishedDoi":"10.21203/rs.3.rs-7255302/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7255302/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStalk rot is a widespread soil-borne disease that severely impacts both the yield and quality of maize. The occurrence of stalk rot is closely related to the structure and composition of the stalk. Microstructure analysis revealed that the disease-resistant inbred line (QM1) presented a more organized and tidy arrangement of vascular bundles, along with an increased area and greater cortex thickness than did the susceptible inbred line (HZ4). To explore the regulatory loci of stalk rot-associated stalk fiber components, genome-wide association studies (GWASs), bulk segregation analysis (BSA), and localization approaches were used to explore the regulatory loci of maize stalk rot-associated stalk fiber components. A total of 20 SNPs appeared to be connected in controlling maize\u0026rsquo;s disease defense response. \u003cem\u003ePZE-105083600\u003c/em\u003e at Chr5, and \u003cem\u003ePZE-107034235\u003c/em\u003e at Chr7 were identified as candidate loci for regulating lignin content. The fluorescence quantification results revealed that the expression of two genes, \u003cem\u003ePZE-105083600\u003c/em\u003e and \u003cem\u003ePZE-107034235\u003c/em\u003e, was significantly greater in the disease-resistant lines than in the disease-susceptible materials. The results revealed that the expression of these two genes was significantly greater in the disease-resistant lines than in the disease-susceptible materials. This study is highly important for further investigations of regulatory genes related to the fiber composition of maize rot-associated stalks and the selection and breeding of disease-resistant varieties.\u003c/p\u003e","manuscriptTitle":"Combination Genome-wide Association Study and Bulked Segregant Analysis Reveals the Loci Controlling Stalk Fiber Traits Related to Stalk Rot in Maize","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-20 18:09:59","doi":"10.21203/rs.3.rs-7255302/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-01T19:08:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-27T07:27:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105207362571109357179508067746036296447","date":"2025-08-26T06:45:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-26T04:50:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-24T14:02:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121377957686780674946187573145981927976","date":"2025-08-24T10:34:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260642448616904200192223131637436630953","date":"2025-08-14T06:55:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266323077351643631790292913352050042164","date":"2025-08-12T04:20:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251632561055820231589414643355531495903","date":"2025-08-12T03:46:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-12T03:39:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-06T21:12:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-06T03:59:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-06T03:58:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-07-30T17:18:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2efacedf-b79d-49c9-8dc4-6c49fde4bec8","owner":[],"postedDate":"August 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-16T10:38:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-20 18:09:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7255302","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7255302","identity":"rs-7255302","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}