Genome-wide identification and transcriptome analysis revealed candidate genes controlling plant height in soybean using YHSBLP

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In this study, we undertook an extensive phenotypic assessment of 339 soybean breeding lines adapted to the Yangtze-Huai region of China, evaluated across six different environments over a period of five years. To identify genomic regions associated with PH, we conducted a genome-wide association study (GWAS) utilizing two robust statistical models: the compressed mixed linear model (CMLM) and the Fixed and Random Model Circulating Probability Unification model (FarmCPU). This analysis employed a high-density marker set comprising over 60,000 single nucleotide polymorphism (SNP) markers, facilitating the detection of significant quantitative trait nucleotide (QTN) regions associated with PH. As a result, five stable QTN regions were identified in association with PH. Among these, two regions overlapped with previously reported quantitative trait loci or well-known soybean PH genes, specifically Dt1 on and E2 . To identify candidate genes for qPh08-1 loci significantly associated with PH, we identified 135 putative genes within the Gm08_15013896 block. Transcriptome data analysis revealed that the expression levels of Glyma.08g192700 were significantly higher in three extremely short lines (SLs) compared to high lines (HLs) during the V2 developmental stage of the stem apical meristem (SAM) in soybean. DNA sequencing analysis successfully determined the nucleotide sequences of the initial six promoter regions of Glyma.08g192700 . A 7-base pair insertion (TG→CGCCTGCCG) was identified at position 153 of the third exon, distinguishing HLs from SLs. This insertion introduces a premature termination codon (TAA) at position 50 of the fourth exon, resulting in the HLs variant encoding a truncated protein of only 136 amino acid residues. Therefore, Glyma.08g192700 is likely the most probable gene involved in qPh08-1 . soybean genome-wide association analysis plant height transcriptome analysis candidate genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Soybean [ Glycine max (L.) Merr.] is a crop of global significance, serving as a crucial source of edible oil, protein, and industrial raw materials, with its cultivation playing an essential role in promoting food security and sustainable agriculture (Yang et al. 2025). Enhancing yield per unit area necessitates a focus on plant height (PH), an important agronomic trait that significantly influences canopy architecture, light interception, and ultimately, yield potential(Li et al. 2024). Nevertheless, PH is a complex quantitative trait governed by multiple genes with minor effects, posing challenges in elucidating its genetic architecture and identifying the underlying molecular mechanisms(Yang et al. 2021). This complexity is compounded by the intricate interactions among genetic factors, environmental conditions, and genotype-by-environment interactions (G×E), which further complicate the accurate prediction and manipulation of PH(Shu et al. 2023). Recent advancements in high-throughput phenotyping and genomic technologies have begun to mitigate these challenges, facilitating more precise characterization of PH and its genetic determinants(Varala et al. 2011; Wu et al. 2010; Santana et al. 2022). Recent advancements in molecular biology and statistical methodologies for quantitative trait loci (QTL) mapping have markedly improved our capacity to analyze the genetic architecture of PH in soybean. PH is a polygenic trait influenced by multiple QTLs, as demonstrated by numerous studies(Yu et al. 2020; Wu et al. 2022; Wang et al. 2020b). To date, more than 300 QTLs associated with PH have been cataloged in the SoyBase database (http://www.soybase.org/), reflecting the expanding body of genetic research in this domain. For instance, Specht et al. (Specht et al. 2001) identified nine PH QTLs distributed across eight chromosomes using a recombinant inbred line (RIL) population comprising 236 individuals. Similarly, Yao et al. (Yao et al. 2015) detected nine PH QTLs across six linkage groups using an F 2 -derived population of 236 individuals. In a broader context, Fang et al. (Zhang et al. 2021) identified 245 loci associated with 84 agronomic traits and elucidated the genetic networks underlying phenotypic trait correlations through genome-wide association studies (GWAS) conducted on 809 soybean accessions. Furthermore, Fang et al. (Fang et al. 2020) reported 48 PH QTLs using 156 recombinant inbred lines derived from the cross between "Dongnong L13" and "Henong 60." These QTLs were identified across nine environments at four locations over six years, utilizing interval mapping and inclusive composite interval mapping methods. Collectively, these studies underscore the efficacy of integrating high-density genotyping, multi-environment trials, and advanced statistical models in elucidating the genetic determinants of PH in soybean. The integration of transcriptomics and genomics has emerged as a pivotal strategy for the identification of candidate genes, offering robust technical support for elucidating the genetic underpinnings of complex traits. By synthesizing GWAS with transcriptomic data, researchers can more accurately pinpoint genetic loci linked to target traits and identify functional candidate genes. For instance, in investigations concerning PH, GWAS can detect significant QTN regions, while transcriptomic analyses can uncover the expression patterns of genes within these regions and their dynamic variations across different developmental stages or environmental conditions(Zhao et al. 2024; Yang et al. 2024b; Jia et al. 2024; Wang et al. 2021; Bai et al. 2022; Xu et al. 2024). Likewise, in studies on crop stress resistance, Ullah et al.(Ullah et al. 2024) identified a critical gene associated with drought stress response through the integration of GWAS and transcriptomic data and subsequently validated its function in rice. By analyzing transcriptomic data from individuals exhibiting extreme phenotypes, such as tall versus short plants or drought-resistant versus drought-sensitive specimens, researchers can identify genes with significantly differential expression across specific tissues or developmental stages, thereby refining the selection of candidate genes (Ban et al. 2019). Furthermore, the integration of promoter region and coding sequence variation analyses can elucidate the effects of functional mutations, such as insertions, deletions, or premature termination codons, on gene function, thus providing additional validation for candidate genes(Yi et al. 2018; Zhang et al. 2024). For example, Shi et al. (Shi et al. 2021)successfully pinpointed a crucial gene linked to seed dormancy by combining GWAS with transcriptomic data, and they further clarified its regulatory mechanisms under varying environmental conditions. In research focused on yield-related traits, Zhao et al. (xuezhao et al. 2024)integrating genome wide association study transcriptome and metabo1ome revea1 nove1 qt1 and candidate genes that contro1 protein con10t in soybean, subsequently validating its potential application in high-yield breeding through functional experiments. This multi-omics integration strategy not only significantly improves the accuracy of candidate gene identification but also provides new perspectives for deciphering the genetic and molecular mechanisms of complex traits. In the future, with the development of emerging technologies such as single-cell transcriptomics and spatial transcriptomics, candidate gene identification will become even more precise and efficient. This study seeks to elucidate the genetic determinants of soybean PH through the integration of transcriptomic and genomic methodologies. Employing GWAS with 61,174 SNPs, we identified 5 QTNs and screened potential candidate genes via transcriptomic analysis of phenotypic extremes, such as high versus short lines. Functional mutations, including insertions/deletions (indels) and premature stop codons, were characterized through analyses of promoter regions and coding sequences, with subsequent experimental validation of candidate genes. Thus, Glyma.08g192700 might be the most likely gene involving qPh08-1 . The results furnish valuable genetic resources and a theoretical framework for the molecular breeding of plant height, thereby facilitating the development of high-yield soybean cultivars. This multi-omics strategy enhances the precision of candidate gene identification and provides insights into the genetic mechanisms underlying complex agronomic traits. Materials and methods A total of 339 breeding lines, which have been specifically selected from the Chinese Yangtze-Huai region and are primarily utilized for PH, were employed in this experiment. This population is known as the Yangtze-Huai Soybean Breeding Line Population (YHSBLP), with most of the lines being consistent with those reported by Chang et al.(Chang et al. 2018) and Yan et al. (Yan et al. 2021). The seeds used for the panel were obtained from the National Center for Soybean Improvement at Nanjing Agricultural University in Jiangsu Province, China. Plant materials and field trials All soybean materials were planted in a randomized complete block design with a single row plot with 70 cm × 80 cm and three replications. The field experiments were conducted at Yancheng (YC) City (33 ◦ 21’N and 120 ◦ 09’E) in Jiangsu province during June 2017, June 2018, June and July 2019 (2019a and 2019b), July 2020 and July 2021. The length from the cotyledonary node to the peak of the main stem represents the PH. The field management was conducted under local cultural practice. Phenotypic evaluation and statistical analysis PH was the averages of three measurements per plot selected randomly before harvest in the field for each replication. Analysis of variance (ANOVA) was conducted for the phenotypic values of PH in different environments using the SPSSAU (https://spssau.com/index.html). All parameters were estimated from the expected mean squares in the ANOVA. Pearson correlation coefficients of the PH among the environment were estimated and visualized with Corrplot package in R. The best linear unbiased estimate (BLUE) of individual lines for PH was determined using the R program lme4 to reduce the effects of environmental variation(Bates et al. 2014). OriginPro 2020 Statistical Software was used to plot the frequency distribution of phenotypic BLUE (Origin Corporation, Northampton, MA, United States). The Broad heritability (h 2 ) is computed using the formula in Yang et al.(Yang et al. 2019). SNPs polymorphism, genotyping and haplotype block estimation High-throughput SNPs (Wm82.a1.v1.1) were generated of 339 lines of YHSBLP, and this sequencing was carried out by Beijing Genomics Institution, Shenzhen, China. The SNP data was screened at a rate of missing and heterozygous allele calls ≤ 30%, and then the missing genotypes were imputed using fastPHASE software(Scheet and Stephens 2006). From 87,308 SNPs, 61,174 SNPs with minor allele frequencies (MAF) of less than 5% were chosen for the GWAS analysis of 339 accessions. The SNP dataset used in this investigation is accessible at the National Center for Soybean Improvement's website. pLINK V2.0 software was used to determine genotype and SNP markers. TASSEL 5.0 was used to create a Neighborjoining (NJ) tree using a pairwise distance matrix produced from Nei's genetic distance for all polymorphic SNPs, and the Kinship was also examined in this program (Bradbury et al. 2007). Genome-wide association studies For the association mapping using the 339 soybean lines, a total of 61,174 SNPs of genome with MAF >5% were used. The CMLM (compressed mixed linear model) and FarmCPU were used to detect significant marker-trait associations in GAPIT version 3.0(Turner 2014). The Kinship matrix (K) and the first four axes of the PCA were employed as variables in the CMLM model to reduce false positives and boost statistical power. To reduce false positives in the FarmCPU model, the kinship matrix (K) and pseudo QTNs were utilized as variables. In this investigation, the significant level was established at 0.0001, which was the same as Chang et al. (Chang et al. 2018). The R software package qqman was used to create Manhattan plots. Prediction of candidate genes within major QTNs Seven phenotypic datasets will be available for PH, comprising measured data from six distinct situations as well as BLUE values. QTNs were defined as SNPs that were co-localized in at least three datasets and had a significance level greater than 0.05. We enlarged and chose the region of SNP site that has a significance of more than 0.001 upstream and downstream based on the LD distance. The possible candidate genes were predicted within major significant QTN regions based on functional annotations available in the TAIR4 and SoyBase databases, as well as available literature. According to the LD distance, we extended and selected the region of 500 kb upstream and downstream of the QTNs as 1.0 Mb QTN regions. The particular candidate genes were considered basing on the predictive genes source of Wm82.a4.v1 genome (https://www.soybase.org/, accessed on 4 February 2025). The SNPs closely linked to the PH detected in multiple environments were mapped to the reference physical map of soybean genome according to the sequence position (https://www.soybase.org). The haplotype domain of the association site was searched for potential genes for the target characteristics, and the genes in the association area were compared to the KEGG databases to acquire annotation information for the genes in the association region. Their functions were predicated using protein-protein BLAST search tool of NCBI (http://blast.ncbi.nlm.nih.gov/Blast.cgi). The expression data of genes were derived from sequencing data of transcripts from V2 development stages of six lines include three high lines（HLs: F061, F163 and F209）and three short lines（SLs: F011, F131 and F260）selected from 339 accessions with significant differences in PH traits. The gene expression and phenotype were statistically analyzed by SPSSAU. During the transcriptome sequencing process, each sample contains three biological replicates. A threshold of |log2 foldchange| ≥ 1 with p-value < 0.05 was adopted to screen for differentially expressed genes within the stable loci. Candidate gene analysis To identify candidate genes related to the PH, the gene expression data set includes those from three high lines（HLs: F061, F163 and F209）and three short lines（SLs: F011, F131 and F260）from YHSBLP at V2 developmental stage of stem apical meristem (SAM) (Fig.1). For identifying the sequence difference of the candidate genes with different expressions, the total DNA was extracted from leaves of six lines using the EasyPure Plant DNA Kit, and the genomic segments of Glyma.08g192700 was sequenced. The CDS sequences of three high lines（HLs: F061, F163 and F209）, three short lines（SLs: F011, F131 and F260） and Williams 82 were aligned using SnapGene software. The primers were designed using Primer Premier 5.0. Results Phenotypic variation in the YHSBLP for PH trait PH demonstrated considerable phenotypic variation across the six environments studied. The phenotypic attributes of PH for the YHSBLP are detailed in Table 1 and Fig.2. The observed PH values spanned from 22.30 to 106.67 cm across all environments, with a mean ± standard deviation (SD) of 59.32 ± 8.42 cm. The coefficient of variation (CV) ranged from 14.26% to 22.5% (Table 1). The continuous distribution patterns of PH across all environments (Fig.2) suggest that this trait is a typical quantitative characteristic governed by multiple genes. The high broad-sense heritability ( h ²) estimate of 84.33% for PH indicates a predominant genetic influence on this trait. Additionally, the significant genotype × environment interaction suggests that environmental conditions modulate the expression of PH. Collectively, these findings indicate that while PH is primarily under genetic control, it remains susceptible to environmental influences. Table 1 Descriptive statistics for PH across six different environments in the YHSBLP ENV. Number Mean SD Min Max skew kurtosis CV(%) h 2 (%) BLUE 339 59.28 8.42 36.40 82.60 -0.04 -0.26 14.20 84.33 2017 339 81.21 12.49 50.67 100.00 -0.57 -0.55 15.38 2018 324 53.28 11.99 28.00 96.67 0.54 0.32 22.50 2019a 329 64.15 12.89 30.00 106.67 0.42 -0.08 20.09 2019b 331 59.71 10.64 22.30 90.00 0.05 0.24 17.82 2020 338 50.73 8.74 25.00 76.00 0.04 0.00 17.23 2021 321 49.01 9.91 23.00 79.00 0.21 0.09 20.22 GWAS analysis for PH using CMLM and FarmCPU CMLM and FarmCPU analyses were conducted to identify significant SNPs associated with PH. Consequently, five QTNs demonstrated significant associations [−log10(P) ≥ 4.00] with the PH BLUE values (Table 2 and Fig. 3). Consistent with the characteristics of a quantitative trait, no QTN was consistently detected across all seven environments, underscoring the complex nature of the PH trait. From the initial 14 SNPs, a total of five QTNs were identified by clustering them within a 1 Mb distance. The QTNs were designated based on their chromosomal location; for instance, qPh08-1 denotes the first QTN on chromosome 8 (Table 2). To corroborate the loci identified by the CMLM method, a multi-locus model FarmCPU approach was employed for GWAS, validating out four of five the QTNs identified by CMLM. Furthermore, these five QTNs were detectable in at least one single environment, suggesting their enhanced reliability (as shown in Table 2). Among the five reliable QTNs, qPh10-1 and qPh19-1 were mapped to regions previously associated with PH, whereas qPh08-1 , qPh14-1 and qPh17-1 were identified as novel QTLs for PH. Table 2 The QTL regions associated with PH trait via CMLM and FarmCPU method in the YHSBLP Locus Name Chr. SNP (Wm82.a1) MAF No. Pos.(Mb) CMLM (-Log10(P)) FarmCPU (-Log10(P)) R 2 (%) ENV Reported QTL/Gene qPh08-1 08 Gm08_15013896 0.06 5 14.97-15.07 5.74 4.18 5.61 1, 3 qPh10-1 10 Gm10_44346474 0.34 3 44.23-46.91 4.48 8.31 4.20 6 PH18-2, PH19-2, PH23-4, PH29-3, PH31-2/ E2 qPh14-1 14 Gm14_48049172 0.20 1 48.05-48.06 4.23 3.93 qPh17-1 17 Gm17_33743028 0.07 1 33.73-33.79 4.38 9.04 4.10 4 qPh19-1 19 Gm19_44938772 0.27 4 44.93-44.96 7.92 10.30 8.11 1, 2, 3 PH1-1, PH3-1, PH4-2, PH4-4, PH6-1, PH8-3, PH10-4, PH13-8/ Dt1 Note: Chr. refers to chromosome; No. refers to number; Pos. refers to position; ENV refers to environment Prediction of Candidate Genes The most prominent QTN, Gm19_44938772, located within the qGm19-1 region on chromosome 19, exhibited the highest −log10(P) value (7.92) and was associated with PH. This QTN overlapped with Dt1 , a well-established gene known to regulate stem growth habit in soybean. Additionally, E2 , a gene recognized for its role in regulating flowering and maturation duration in soybean, is situated within the qPh10-1 region and is considered a candidate gene for PH. To identify candidate genes for loci significantly associated with PH on chromosome 08, we selected putative genes marked by the most reliable linkage blocks. Within the Gm08_15013896 block (spanning 14.43-15.45 Mb), 135 putative genes associated with PH were identified (see Table 3). To assess the potential roles of these candidate genes in regulating soybean PH, gene-based association studies and transcriptome sequencing were conducted. We identified a total of 135 genes, of which 120 were expressed in the SAM, while 15 were not expressed in SAM, according to the transcriptome data. Based on lines height phenotypes, two groups were selected: three high lines (HLs: F061, F163 and F209) and three short lines (SLs: F011, F131 and F260). Transcriptome data analysis result showed that the expression levels of Glyma.08g192700 among 120 genes in extremely short lines were significantly higher than in high lines, Fig.4. Cloning and Sequence Alignment of Glyma.08g192700 The gene structure of Glyma.08g192700 was analyzed in three high lines (HLs: F061, F163 and F209) and three short lines (SLs: F011, F131 and F260). According to the soybean reference genome Wm82.a4.v1, this gene is situated on chromosome 8 (Gm08:15578505-15585211). The genomic sequence encompasses 6,706 base pairs (bp), with a coding sequence (CDS) of 1,725 bp, comprising 17 exons and 16 introns (Figure 5A). Polymerase chain reaction (PCR) amplification and sequencing of Glyma.08g192700 , including its flanking regions (1,000 bp upstream and 800 bp downstream), were conducted using overlapping primers. The nucleotide sequences of the initial six promoter regions were successfully elucidated through DNA sequencing analysis. A 7-bp insertion (TG→CGCCTGCCG) was identified at position 153 of the third exon, distinguishing the HLs from the SLs (Figure 5B). This insertion results in the introduction of a premature termination codon (TAA) at position 50 of the fourth exon (Figure 5C-D). Consequently, the HLs variant encodes a truncated protein consisting of only 136 amino acid residues. Bioinformatics analysis using SoyBase (http://www.soybase.org) indicates that Glyma.08g192700 belongs to the TCP-1/cpn60 chaperonin family. Thus, Glyma.08g192700 might be the most likely gene involving qPh08-1 . Discussion GWAS are potent methodologies for elucidating the genetic underpinnings of complex traits in natural populations(Zhang et al. 2019 ; Wen et al. 2018 ; Yang et al. 2024b ; Wang et al. 2020a ). Conversely, linkage analysis in natural populations can effectively mitigate the incidence of false positives, a notable limitation of GWAS. However, the results from linkage analysis often encompass broad intervals, complicating the identification of target genes. The incorporation of multi-year and multi-environment data analysis enhances the precision of QTL localization and effect estimation (Zhang et al. 2019 ), thereby aiding in the identification of stable QTLs. In our study, we employed multi-locus GWAS to identify QTNs associated with PH in soybean. The GWAS analysis revealed five QTNs for PH, as detailed in Table 2 . These include qPh08-1 on chromosome 8 at 14.97–15.07 Mb; qPh10-1 at 44.23–46.91 Mb on chromosome 10; qPh14-1 at 48.05–48.06 Mb on chromosome 14; qPh17-1 at 33.73–33.79 Mb on chromosome 17; and qPh19-1 at 44.93–44.96 Mb on chromosome 19. In this study, two of the QTNs were found to overlap with previously identified QTLs or genes known to be associated with PH in soybean. Notably, the locus qPh08-1 , qPh17-1 , qPh14-1 , identified on chromosome 14, has not been previously reported on genetic maps, suggesting it represents a novel QTL. Furthermore, the locus qPh19-1 , located at 44.93–44.96 Mb on chromosome 19, exhibits similarity to previously reported QTLs, specifically Pod mat 13 − 6 and Plant height 4 − 2 and Plant height 13 − 8 ( Dt1 )(Zhang et al. 2019 ; Wen et al. 2018 ), and demonstrates a strong association with PH. The integration of transcriptomic and genomic methodologies in this study has yielded significant insights into the genetic architecture of soybean PH, a complex trait modulated by numerous genetic and environmental factors. Our results corroborate prior research that highlights the polygenic nature of PH, as demonstrated by the identification of multiple QTNs and candidate genes(Zhang et al. 2019 ; Wen et al. 2018 ; Yang et al. 2024b ; Wang et al. 2020a ; Assefa et al. 2019 ). The successful identification of significant QTNs through GWAS emphasizes the efficacy of high-density SNP markers in elucidating complex traits, in agreement with findings reported in other crops such as rice and wheat, and so on(Zhou et al. 2023 ; Sitoe et al. 2022 ; Han et al. 2017 ; Yang et al. 2024a ). The structural and functional analysis of Glyma.08g192700 offers compelling evidence supporting its role as a key candidate gene underlying the qPh08-1 locus, which is associated with PH in soybean. This gene is situated on chromosome 8, encoding a protein that belongs to the TCP-1/cpn60 chaperonin family. Proteins in this family are known to play critical roles in protein folding and stress responses, suggesting a potential functional link to plant growth and development(Danisman et al. 2013 ; Chen et al. 2016 ; Suzuki et al. 2009 ; Xu et al. 2014 ). The identification of a 7-base pair insertion at position 153 of the third exon in HLs represents a significant finding. This insertion introduces a premature termination codon (TAA) at position 50 of the fourth exon, resulting in a truncated protein consisting of only 136 amino acid residues in HLs. Such structural alterations are likely to impair the protein's function, potentially contributing to the observed phenotypic differences in plant height between HLs and SLs. This finding aligns with previous studies demonstrating that premature termination codons often lead to loss-of-function mutations, which can significantly impact agronomic traits(Yang et al. 2019 ). The differential expression of Glyma.08g192700 in the SAM during the V2 developmental stage further underscores its functional significance. The markedly elevated expression levels in SLs compared to HLs imply that this gene may function as a negative regulator of PH, with its truncated form in HLs potentially compromising its regulatory capacity. This hypothesis aligns with the established role of TCP family genes in influencing plant architecture and growth(Jone et al. 2025 ; He et al. 2020 ; Chai et al. 2017 ). The integration of genomic and transcriptomic data in this study underscores the efficacy of multi-omics approaches in elucidating complex traits. Through the combination of gene structure analysis, expression profiling, and functional annotation, Glyma.08g192700 has been identified as a strong candidate for qPh08-1 . Nonetheless, further functional validation, such as CRISPR/Cas9-mediated gene editing or transgenic complementation assays, is required to confirm its role in the regulation of PH. In summary, this study enhances our comprehension of the genetic mechanisms underlying soybean PH and identifies a valuable candidate gene for molecular breeding. The findings highlight the significance of structural variations within coding regions and their influence on gene function, thereby offering novel insights into the molecular basis of agronomic traits. Future research should investigate the broader regulatory network of Glyma.08g192700 and its interactions with other genes to comprehensively elucidate its role in plant growth and development. Conclusions In this study, we conducted a comprehensive analysis of the genetic determinants of PH, a vital agronomic trait in soybean, by integrating phenotypic assessments, GWAS, and transcriptomic methodologies. Employing a high-density SNP marker set alongside two robust statistical models, we identified five stable QTNs significantly associated with PH. Within the Gm08_15013896 locus on chromosome 8, we screened 135 candidate genes, and transcriptomic analysis indicated that the expression levels of Glyma.08g192700 were markedly elevated in the SAM at the V2 developmental stage in extremely lines. Through sequence alignment of the coding regions of the gene, Glyma.08g192700 was ultimately identified as the key candidate gene for the qPh08-1 locus. These findings will contribute to the accelerated development of high-yielding soybean varieties with ideal plant architecture, thereby supporting global food security. Declarations FUNDING This work was supported by grants from the Natural Science Foundation of China (32101704), the Major Project of Yancheng Science and Technology (YCBK2024055，YCBK2025070), the National Key Research and Development Program of China (2024YFD1201400)，the Core Technology Development for Breeding Program of Jiangsu Province (JBGS-2021-014), the Major Project of Anhui Provincial Education Department (2023AH040279), the Open fund of Anhui Provincial Key Laboratory for Crop Quality Improvement (2024ZW001), the Coastal Research Foundation Project (YHS202001). Author Contributions: All authors contributed to the study’s conception and design. H.Y. and Y.Z. designed the research; H.Y. and S.X. conducted the data analysis, wrote, and revised the manuscript; W.Y. and Q.H. conducted the bioinformatics analysis; H.S., T.Z., Y.Z. and W.Y. modified the manuscript. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data are available in the manuscript and in the Supplementary Materials. Conflicts of Interest: The authors declare no conflicts of interest. Disclaimer/Publisher ’ s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of Euphytica and/or the editor(s). Euphytica and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. References Assefa T, Otyama PI, Brown AV, Kalberer SR, Kulkarni RS, Cannon SB (2019) Genome-wide associations and epistatic interactions for internode number, plant height, seed weight and seed yield in soybean. 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Genet Mol Res 14 (2):6101-6109. https://doi.org/10.4238/2015.June.8.8 Yi L, Chen C, Yin S, Li H, Li Z, Wang B, King GJ, Wang J, Liu K (2018) Sequence variation and functional analysis of a FRIGIDA orthologue ( BnaA3.FRI ) in Brassica napus . BMC Plant Biol 18 (1):32. https://doi.org/10.1186/s12870-018-1253-1 Yu M, Liu ZH, Yang B, Chen H, Zhang H, Hou DB (2020) The contribution of photosynthesis traits and plant height components to plant height in wheat at the individual quantitative trait locus level. Sci Rep 10 (1):12261. https://doi.org/10.1038/s41598-020-69138-0 Zhang G, Wang R, Ma J, Gao H, Deng L, Wang N, Wang Y, Zhang J, Li K, Zhang W, Mu F, Liu H, Wang Y (2021) Genome-wide association studies of yield-related traits in high-latitude japonica rice. BMC Genom Data 22 (1):39. https://doi.org/10.1186/s12863-021-00995-y Zhang T, Wu T, Wang L, Jiang B, Zhen C, Yuan S, Hou W, Wu C, Han T, Sun S (2019) A combined linkage and GWAS analysis identifies QTLs linked to soybean seed protein and oil content. Int J Mol Sci 20 (23). https://doi.org/10.3390/ijms20235915 Zhang Z, Ma J, Yang X, Liang S, Liu Y, Yuan Y, Liang Q, Shen Y, Zhou G, Zhang M, Tian Z, Liu S (2024) Natural GmACO1 allelic variations confer drought tolerance and influence nodule formation in soybean. Abiotech 5 (3):351-355. https://doi.org/10.1007/s42994-024-00160-w Zhao X, Zhang Y, Wang J, Zhao X, Li Y, Teng W, Han Y, Zhan Y (2024) GWAS and WGCNA analysis uncover candidate genes associated with oil content in soybean. Plants, 13(10), 1351. https://doi.org/10.3390/plants13101351 Zhou C, Xiong H, Fu M, Guo H, Zhao L, Xie Y, Gu J, Zhao S, Ding Y, Li Y, Li X, Liu L (2023) Genetic mapping and identification of Rht8-B1 that regulates plant height in wheat. BMC Plant Biol 23 (1):333. https://doi.org/10.1186/s12870-023-04343-3 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Apr, 2026 Reviews received at journal 08 Apr, 2026 Reviews received at journal 26 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers invited by journal 16 Mar, 2026 Editor assigned by journal 13 Mar, 2026 Submission checks completed at journal 13 Mar, 2026 First submitted to journal 12 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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02:54:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9109533/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9109533/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105000400,"identity":"bed0b029-9556-4ff9-a2b1-53ef295083b6","added_by":"auto","created_at":"2026-03-19 16:40:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":600386,"visible":true,"origin":"","legend":"\u003cp\u003eThree high lines（HLs: F061, F163 and F209）and three short lines（SLs: F011, F131 and F260）\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9109533/v1/6ba1c541359066f0cd630428.png"},{"id":105000401,"identity":"5a1139a7-4798-4ae0-86d4-06ed3d131e6b","added_by":"auto","created_at":"2026-03-19 16:40:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":110677,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency distribution and correlation analysis of PH in the YHSBLP\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9109533/v1/d1ef22b6868ffbc750307881.png"},{"id":105000402,"identity":"391395b1-1311-4bcf-b2b4-5985591f1d6d","added_by":"auto","created_at":"2026-03-19 16:40:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":393357,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan and quantile–quantile plots for PH in six different environments and BLUPs. Red Line represents the significance threshold as determined by Bonferroni multiple comparisons corrections equivalent to –log\u003csub\u003e10\u003c/sub\u003e(\u003cem\u003ep\u003c/em\u003e) D=4.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9109533/v1/c30e23eb733f1003235bb82c.png"},{"id":105000404,"identity":"ed3788f2-086d-4416-9e99-2a464ca1e3ab","added_by":"auto","created_at":"2026-03-19 16:40:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":93183,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of candidate genes in the region around \u003cem\u003eqPh08-1\u003c/em\u003e on Chr. 8 in SAM tissues and V2 period of soybean of six lines. A The heat map for expression level of candidate gene in the region of \u003cem\u003eqPh08-1\u003c/em\u003e in SAM tissues and V2 period of soybean of six lines. B represent the expression level of \u003cem\u003eGlyma.08g192700\u003c/em\u003ein six lines and V2 period of soybean, respectively. Row-Z score in “a” is determined based on the RPKM(Reads/Kb/Million)-normalized log102-transformed transcript count data.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9109533/v1/460fb03ed964fe54b0ce6151.png"},{"id":105035135,"identity":"97d7d69d-1f86-4b61-b9b9-2562aca6f4f3","added_by":"auto","created_at":"2026-03-20 07:25:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":356793,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the sequence differences of \u003cem\u003eGlyma.08g192700\u003c/em\u003ebetween HLs and SLs. A: The gene structure of\u003cem\u003e Glyma.08g192700.\u003c/em\u003e B: The coding sequence differences in \u003cem\u003eGlyma.08g192700\u003c/em\u003e between the HLs and SLs in Exon 3. C: The coding sequence differences in \u003cem\u003eGlyma.08g192700\u003c/em\u003e between the HLs and SLs in Exon. 4. D: Alignment of the gene \u003cem\u003eGlyma.08g192700\u003c/em\u003eamino acids of HLs and SLs.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9109533/v1/50113cf52653ba6d95768977.png"},{"id":105036851,"identity":"40214071-11fb-4ee1-bb4e-0367db2bd870","added_by":"auto","created_at":"2026-03-20 07:36:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2413420,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9109533/v1/5ec5fe1c-d75d-4575-a14b-2cd97db37f87.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide identification and transcriptome analysis revealed candidate genes controlling plant height in soybean using YHSBLP","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoybean [\u003cem\u003eGlycine max\u003c/em\u003e (L.) Merr.] is a crop of global significance, serving as a crucial source of edible oil, protein, and industrial raw materials, with its cultivation playing an essential role in promoting food security and sustainable agriculture (Yang et al. 2025). Enhancing yield per unit area necessitates a focus on plant height (PH), an important agronomic trait that significantly influences canopy architecture, light interception, and ultimately, yield potential(Li et al. 2024). Nevertheless, PH is a complex quantitative trait governed by multiple genes with minor effects, posing challenges in elucidating its genetic architecture and identifying the underlying molecular mechanisms(Yang et al. 2021). This complexity is compounded by the intricate interactions among genetic factors, environmental conditions, and genotype-by-environment interactions (G\u0026times;E), which further complicate the accurate prediction and manipulation of PH(Shu et al. 2023). Recent advancements in high-throughput phenotyping and genomic technologies have begun to mitigate these challenges, facilitating more precise characterization of PH and its genetic determinants(Varala et al. 2011; Wu et al. 2010; Santana et al. 2022).\u003c/p\u003e\n\u003cp\u003eRecent advancements in molecular biology and statistical methodologies for quantitative trait loci (QTL) mapping have markedly improved our capacity to analyze the genetic architecture of PH in soybean. PH is a polygenic trait influenced by multiple QTLs, as demonstrated by numerous studies(Yu et al. 2020; Wu et al. 2022; Wang et al. 2020b). To date, more than 300 QTLs associated with PH have been cataloged in the SoyBase database (http://www.soybase.org/), reflecting the expanding body of genetic research in this domain. For instance, Specht et al. (Specht et al. 2001) identified nine PH QTLs distributed across eight chromosomes using a recombinant inbred line (RIL) population comprising 236 individuals. Similarly, Yao et al. (Yao et al. 2015) detected nine PH QTLs across six linkage groups using an F\u003csub\u003e2\u003c/sub\u003e-derived population of 236 individuals. In a broader context, Fang et al. (Zhang et al. 2021) identified 245 loci associated with 84 agronomic traits and elucidated the genetic networks underlying phenotypic trait correlations through genome-wide association studies (GWAS) conducted on 809 soybean accessions. Furthermore, Fang et al. (Fang et al. 2020) reported 48 PH QTLs using 156 recombinant inbred lines derived from the cross between \u0026quot;Dongnong L13\u0026quot; and \u0026quot;Henong 60.\u0026quot; These QTLs were identified across nine environments at four locations over six years, utilizing interval mapping and inclusive composite interval mapping methods. Collectively, these studies underscore the efficacy of integrating high-density genotyping, multi-environment trials, and advanced statistical models in elucidating the genetic determinants of PH in soybean.\u003c/p\u003e\n\u003cp\u003eThe integration of transcriptomics and genomics has emerged as a pivotal strategy for the identification of candidate genes, offering robust technical support for elucidating the genetic underpinnings of complex traits. By synthesizing GWAS with transcriptomic data, researchers can more accurately pinpoint genetic loci linked to target traits and identify functional candidate genes. For instance, in investigations concerning PH, GWAS can detect significant QTN regions, while transcriptomic analyses can uncover the expression patterns of genes within these regions and their dynamic variations across different developmental stages or environmental conditions(Zhao et al. 2024; Yang et al. 2024b; Jia et al. 2024; Wang et al. 2021; Bai et al. 2022; Xu et al. 2024). Likewise, in studies on crop stress resistance, Ullah et al.(Ullah et al. 2024) identified a critical gene associated with drought stress response through the integration of GWAS and transcriptomic data and subsequently validated its function in rice.\u0026nbsp;By analyzing transcriptomic data from individuals exhibiting extreme phenotypes, such as tall versus short plants or drought-resistant versus drought-sensitive specimens, researchers can identify genes with significantly differential expression across specific tissues or developmental stages, thereby refining the selection of candidate genes\u0026nbsp;(Ban et al. 2019). Furthermore, the integration of promoter region and coding sequence variation analyses can elucidate the effects of functional mutations, such as insertions, deletions, or premature termination codons, on gene function, thus providing additional validation for candidate genes(Yi et al. 2018; Zhang et al. 2024). For example, Shi et al.\u0026nbsp;(Shi et al. 2021)successfully pinpointed a crucial gene linked to seed dormancy by combining GWAS with transcriptomic data, and they further clarified its regulatory mechanisms under varying environmental conditions. In research focused on yield-related traits, Zhao et al. (xuezhao et al. 2024)integrating genome wide association study transcriptome and metabo1ome revea1 nove1 qt1 and candidate genes that contro1 protein con10t in soybean, subsequently validating its potential application in high-yield breeding through functional experiments. This multi-omics integration strategy not only significantly improves the accuracy\u0026nbsp;of candidate gene identification but also provides new perspectives for deciphering the genetic and molecular mechanisms of complex traits. In the future, with the development of emerging technologies such as single-cell transcriptomics and spatial transcriptomics, candidate gene identification will become even more precise and efficient.\u003c/p\u003e\n\u003cp\u003eThis study seeks to elucidate the genetic determinants of soybean PH through the integration of transcriptomic and genomic methodologies. Employing GWAS with 61,174 SNPs, we identified 5 QTNs and screened potential candidate genes via transcriptomic analysis of phenotypic extremes, such as high versus short lines. Functional mutations, including insertions/deletions (indels) and premature stop codons, were characterized through analyses of promoter regions and coding sequences, with subsequent experimental validation of candidate genes. Thus,\u003cem\u003e\u0026nbsp;Glyma.08g192700\u003c/em\u003e might be the most likely gene involving \u003cem\u003eqPh08-1\u003c/em\u003e. The results furnish valuable genetic resources and a theoretical framework for the molecular breeding of plant height, thereby facilitating the development of high-yield soybean cultivars. This multi-omics strategy enhances the precision of candidate gene identification and provides insights into the genetic mechanisms underlying complex agronomic traits.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eA total of 339 breeding lines, which have been specifically selected from the Chinese Yangtze-Huai region and are primarily utilized for PH, were employed in this experiment. This population is known as the Yangtze-Huai Soybean Breeding Line Population (YHSBLP), with most of the lines being consistent with those reported by Chang et al.(Chang et al. 2018) and Yan et al. (Yan et al. 2021). The seeds used for the panel were obtained from the National Center for Soybean Improvement at Nanjing Agricultural University in Jiangsu Province, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlant materials and field trials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll soybean materials were planted in a randomized complete block design with a single row plot with 70 cm \u0026times; 80 cm and three replications. The field experiments were conducted at Yancheng (YC) City (33\u003csup\u003e◦\u003c/sup\u003e21\u0026rsquo;N and 120\u003csup\u003e◦\u003c/sup\u003e09\u0026rsquo;E) in Jiangsu province during June 2017, June 2018, June and July 2019 (2019a and 2019b), July 2020 and July 2021. The length from the cotyledonary node to the peak of the main stem represents the PH. The field management was conducted under local cultural practice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhenotypic evaluation and statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePH was the averages of three measurements per plot selected randomly before harvest in the field for each replication. Analysis of variance (ANOVA) was conducted for the phenotypic values of PH in different environments using the SPSSAU (https://spssau.com/index.html). All parameters were estimated from the expected mean squares in the ANOVA. Pearson correlation coefficients of the PH among the environment were estimated and visualized with \u003cem\u003eCorrplot \u003c/em\u003epackage in R. The best linear unbiased estimate (BLUE) of individual lines for PH was determined using the R program \u003cem\u003elme4\u003c/em\u003e to reduce the effects of environmental variation(Bates et al. 2014). OriginPro 2020 Statistical Software was used to plot the frequency distribution of phenotypic BLUE (Origin Corporation, Northampton, MA, United States). The Broad heritability (h\u003csup\u003e2\u003c/sup\u003e) is computed using the formula in Yang et al.(Yang et al. 2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNPs polymorphism, genotyping and haplotype block estimation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh-throughput SNPs (Wm82.a1.v1.1) were generated of 339 lines of YHSBLP, and this sequencing was carried out by Beijing Genomics Institution, Shenzhen, China. The SNP data was screened at a rate of missing and heterozygous allele calls \u0026le; 30%, and then the missing genotypes were imputed using fastPHASE software(Scheet and Stephens 2006). From 87,308 SNPs, 61,174 SNPs with minor allele frequencies (MAF) of less than 5% were chosen for the GWAS analysis of 339 accessions. The SNP dataset used in this investigation is accessible at the National Center for Soybean Improvement\u0026apos;s website. pLINK V2.0 software was used to determine genotype and SNP markers. TASSEL 5.0 was used to create a Neighborjoining (NJ) tree using a pairwise distance matrix produced from Nei\u0026apos;s genetic distance for all polymorphic SNPs, and the Kinship was also examined in this program (Bradbury et al. 2007). \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eGenome-wide association studies \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the association mapping using the 339 soybean lines, a total of 61,174 SNPs of genome with MAF \u0026gt;5% were used. The CMLM (compressed mixed linear model) and FarmCPU were used to detect significant marker-trait associations in GAPIT version 3.0(Turner 2014). \u003c/p\u003e\n\u003cp\u003eThe Kinship matrix (K) and the first four axes of the PCA were employed as variables in the CMLM model to reduce false positives and boost statistical power. To reduce false positives in the FarmCPU model, the kinship matrix (K) and pseudo QTNs were utilized as variables. In this investigation, the significant level was established at 0.0001, which was the same as Chang et al. (Chang et al. 2018). The R software package qqman was used to create Manhattan plots.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of candidate genes within major QTNs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeven phenotypic datasets will be available for PH, comprising measured data from six distinct situations as well as BLUE values. QTNs were defined as SNPs that were co-localized in at least three datasets and had a significance level greater than 0.05. We enlarged and chose the region of SNP site that has a significance of more than 0.001 upstream and downstream based on the LD distance. The possible candidate genes were predicted within major significant QTN regions based on functional annotations available in the TAIR4 and SoyBase databases, as well as available literature. According to the LD distance, we extended and selected the region of 500 kb upstream and downstream of the QTNs as 1.0 Mb QTN regions.\u003c/p\u003e\n\u003cp\u003eThe particular candidate genes were considered basing on the predictive genes source of Wm82.a4.v1 genome (https://www.soybase.org/, accessed on 4 February 2025). The SNPs closely linked to the PH detected in multiple environments were mapped to the reference physical map of soybean genome according to the sequence position (https://www.soybase.org). The haplotype domain of the association site was searched for potential genes for the target characteristics, and the genes in the association area were compared to the KEGG databases to acquire annotation information for the genes in the association region. Their functions were predicated using protein-protein BLAST search tool of NCBI (http://blast.ncbi.nlm.nih.gov/Blast.cgi). The expression data of genes were derived from sequencing data of transcripts from V2 development stages of six lines include three high lines（HLs: F061, F163 and F209）and three short lines（SLs: F011, F131 and F260）selected from 339 accessions with significant differences in PH traits. The gene expression and phenotype were statistically analyzed by SPSSAU. During the transcriptome sequencing process, each sample contains three biological replicates. A threshold of |log2 foldchange| \u0026ge; 1 with p-value \u0026lt; 0.05 was adopted to screen for differentially expressed genes within the stable loci.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCandidate gene analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify candidate genes related to the PH, the gene expression data set includes those from three high lines（HLs: F061, F163 and F209）and three short lines（SLs: F011, F131 and F260）from YHSBLP at V2 developmental stage of stem apical meristem (SAM) (Fig.1). For identifying the sequence difference of the candidate genes with different expressions, the total DNA was extracted from leaves of six lines using the EasyPure Plant DNA Kit, and the genomic segments of \u003cem\u003eGlyma.08g192700\u003c/em\u003e was sequenced. The CDS sequences of three high lines（HLs: F061, F163 and F209）, three short lines（SLs: F011, F131 and F260） and Williams 82 were aligned using SnapGene software. The primers were designed using Primer Premier 5.0. \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePhenotypic variation in the YHSBLP for PH trait\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePH demonstrated considerable phenotypic variation across the six environments studied. The phenotypic attributes of PH for the YHSBLP are detailed in Table 1 and Fig.2. The observed PH values spanned from 22.30 to 106.67 cm across all environments, with a mean\u0026nbsp;\u0026plusmn;\u0026nbsp;standard deviation (SD) of 59.32\u0026nbsp;\u0026plusmn;\u0026nbsp;8.42 cm. The coefficient of variation (CV) ranged from 14.26% to 22.5% (Table 1). The continuous distribution patterns of PH across all environments (Fig.2) suggest that this trait is a typical quantitative characteristic governed by multiple genes. The high broad-sense heritability (\u003cem\u003eh\u003c/em\u003e\u0026sup2;) estimate of 84.33% for PH indicates a predominant genetic influence on this trait. Additionally, the significant genotype\u0026nbsp;\u0026times;\u0026nbsp;environment interaction suggests that environmental conditions modulate the expression of PH. Collectively, these findings indicate that while PH is primarily under genetic control, it remains susceptible to environmental influences.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e1\u003c/strong\u003e Descriptive statistics for PH across six different environments in the YHSBLP\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eENV.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eskew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ekurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCV(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eh\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBLUE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e96.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2019a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e106.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2019b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eGWAS analysis for\u0026nbsp;\u003c/strong\u003ePH\u003cstrong\u003e\u0026nbsp;using CMLM and FarmCPU\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCMLM and FarmCPU analyses were conducted to identify significant SNPs associated with PH. Consequently, five QTNs demonstrated significant associations [\u0026minus;log10(P) \u0026ge; 4.00] with the PH BLUE values (Table 2 and Fig. 3). Consistent with the characteristics of a quantitative trait, no QTN was consistently detected across all seven environments, underscoring the complex nature of the PH trait. From the initial 14 SNPs, a total of five QTNs were identified by clustering them within a 1 Mb distance. The QTNs were designated based on their chromosomal location; for instance, \u003cem\u003eqPh08-1\u003c/em\u003e denotes the first QTN on chromosome 8 (Table 2). To corroborate the loci identified by the CMLM method, a multi-locus model FarmCPU approach was employed for GWAS, validating out four of five the QTNs identified by CMLM. Furthermore, these five QTNs were detectable in at least one single environment, suggesting their enhanced reliability (as shown in Table 2). Among the five reliable QTNs, \u003cem\u003eqPh10-1\u003c/em\u003e and \u003cem\u003eqPh19-1\u003c/em\u003e were mapped to regions previously associated with PH, whereas \u003cem\u003eqPh08-1\u003c/em\u003e, \u003cem\u003eqPh14-1\u003c/em\u003eand \u003cem\u003eqPh17-1\u003c/em\u003e were identified as novel QTLs for PH.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e2\u003c/strong\u003e The QTL regions associated with PH trait via CMLM and FarmCPU method in the YHSBLP\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable style=\"width: 5.3e+2pt;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLocus Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChr.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSNP (Wm82.a1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMAF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePos.(Mb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCMLM\u003c/p\u003e\n \u003cp\u003e(-Log10(P))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003cp\u003e(-Log10(P))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eENV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReported QTL/Gene\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eqPh08-1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGm08_15013896\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e14.97-15.07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e5.74\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e4.18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e5.61\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1, 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eqPh10-1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGm10_44346474\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e44.23-46.91\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e4.48\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e8.31\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e4.20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePH18-2, PH19-2, PH23-4, PH29-3, PH31-2/ \u003cem\u003eE2\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eqPh14-1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGm14_48049172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48.05-48.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eqPh17-1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGm17_33743028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e33.73-33.79\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e4.38\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e9.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e4.10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eqPh19-1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGm19_44938772\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.27\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e44.93-44.96\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e7.92\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e10.30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e8.11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1, 2, 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePH1-1, PH3-1, PH4-2, PH4-4, PH6-1, PH8-3, PH10-4, PH13-8/ \u003cem\u003eDt1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: Chr. refers to chromosome; No. refers to number; Pos. refers to position; ENV refers to environment\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of Candidate Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe most prominent QTN, Gm19_44938772, located within the \u003cem\u003eqGm19-1\u003c/em\u003e region on chromosome 19, exhibited the highest \u0026minus;log10(P) value (7.92) and was associated with PH. This QTN overlapped with \u003cem\u003eDt1\u003c/em\u003e, a well-established gene known to regulate stem growth habit in soybean. Additionally, \u003cem\u003eE2\u003c/em\u003e, a gene recognized for its role in regulating flowering and maturation duration in soybean, is situated within the \u003cem\u003eqPh10-1\u003c/em\u003e region and is considered a candidate gene for PH. To identify candidate genes for loci significantly associated with PH on chromosome 08, we selected putative genes marked by the most reliable linkage blocks. Within the Gm08_15013896 block (spanning 14.43-15.45 Mb), 135 putative genes associated with PH were identified (see Table 3). To assess the potential roles of these candidate genes in regulating soybean PH, gene-based association studies and transcriptome sequencing were conducted. We identified a total of 135 genes, of which 120 were expressed in the SAM, while 15 were not expressed in SAM, according to the transcriptome data. Based on lines height phenotypes, two groups were selected: three high lines (HLs: F061, F163 and F209) and three short lines (SLs: F011, F131 and F260). Transcriptome data analysis result showed that the expression levels of \u003cem\u003eGlyma.08g192700\u003c/em\u003e among 120 genes in extremely short lines were significantly higher than in high lines, Fig.4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCloning and Sequence Alignment of \u003cem\u003eGlyma.08g192700\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe gene structure of \u003cem\u003eGlyma.08g192700\u003c/em\u003e was analyzed in three high lines (HLs: F061, F163 and F209) and three short lines (SLs: F011, F131 and F260). According to the soybean reference genome Wm82.a4.v1, this gene is situated on chromosome 8 (Gm08:15578505-15585211). The genomic sequence encompasses 6,706 base pairs (bp), with a coding sequence (CDS) of 1,725 bp, comprising 17 exons and 16 introns (Figure 5A). Polymerase chain reaction (PCR) amplification and sequencing of \u003cem\u003eGlyma.08g192700\u003c/em\u003e, including its flanking regions (1,000 bp upstream and 800 bp downstream), were conducted using overlapping primers. The nucleotide sequences of the initial six promoter regions were successfully elucidated through DNA sequencing analysis. A 7-bp insertion (TG\u0026rarr;CGCCTGCCG) was identified at position 153 of the third exon, distinguishing the HLs from the SLs (Figure 5B). This insertion results in the introduction of a premature termination codon (TAA) at position 50 of the fourth exon (Figure 5C-D). Consequently, the HLs variant encodes a truncated protein consisting of only 136 amino acid residues. Bioinformatics analysis using SoyBase (http://www.soybase.org) indicates that Glyma.08g192700 belongs to the TCP-1/cpn60 chaperonin family. Thus, \u003cem\u003eGlyma.08g192700\u003c/em\u003e might be the most likely gene involving \u003cem\u003eqPh08-1\u003c/em\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGWAS are potent methodologies for elucidating the genetic underpinnings of complex traits in natural populations(Zhang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wen et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). Conversely, linkage analysis in natural populations can effectively mitigate the incidence of false positives, a notable limitation of GWAS. However, the results from linkage analysis often encompass broad intervals, complicating the identification of target genes. The incorporation of multi-year and multi-environment data analysis enhances the precision of QTL localization and effect estimation (Zhang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), thereby aiding in the identification of stable QTLs. In our study, we employed multi-locus GWAS to identify QTNs associated with PH in soybean. The GWAS analysis revealed five QTNs for PH, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. These include \u003cem\u003eqPh08-1\u003c/em\u003e on chromosome 8 at 14.97\u0026ndash;15.07 Mb; \u003cem\u003eqPh10-1\u003c/em\u003e at 44.23\u0026ndash;46.91 Mb on chromosome 10; \u003cem\u003eqPh14-1\u003c/em\u003e at 48.05\u0026ndash;48.06 Mb on chromosome 14; \u003cem\u003eqPh17-1\u003c/em\u003e at 33.73\u0026ndash;33.79 Mb on chromosome 17; and \u003cem\u003eqPh19-1\u003c/em\u003e at 44.93\u0026ndash;44.96 Mb on chromosome 19. In this study, two of the QTNs were found to overlap with previously identified QTLs or genes known to be associated with PH in soybean. Notably, the locus \u003cem\u003eqPh08-1\u003c/em\u003e, \u003cem\u003eqPh17-1\u003c/em\u003e, \u003cem\u003eqPh14-1\u003c/em\u003e, identified on chromosome 14, has not been previously reported on genetic maps, suggesting it represents a novel QTL. Furthermore, the locus \u003cem\u003eqPh19-1\u003c/em\u003e, located at 44.93\u0026ndash;44.96 Mb on chromosome 19, exhibits similarity to previously reported QTLs, specifically \u003cem\u003ePod mat 13\u0026thinsp;\u0026minus;\u0026thinsp;6\u003c/em\u003e and \u003cem\u003ePlant height 4\u0026thinsp;\u0026minus;\u0026thinsp;2\u003c/em\u003e and \u003cem\u003ePlant height 13\u0026thinsp;\u0026minus;\u0026thinsp;8\u003c/em\u003e (\u003cem\u003eDt1\u003c/em\u003e)(Zhang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wen et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and demonstrates a strong association with PH.\u003c/p\u003e \u003cp\u003eThe integration of transcriptomic and genomic methodologies in this study has yielded significant insights into the genetic architecture of soybean PH, a complex trait modulated by numerous genetic and environmental factors. Our results corroborate prior research that highlights the polygenic nature of PH, as demonstrated by the identification of multiple QTNs and candidate genes(Zhang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wen et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Assefa et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The successful identification of significant QTNs through GWAS emphasizes the efficacy of high-density SNP markers in elucidating complex traits, in agreement with findings reported in other crops such as rice and wheat, and so on(Zhou et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sitoe et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Han et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe structural and functional analysis of \u003cem\u003eGlyma.08g192700\u003c/em\u003e offers compelling evidence supporting its role as a key candidate gene underlying the \u003cem\u003eqPh08-1\u003c/em\u003e locus, which is associated with PH in soybean. This gene is situated on chromosome 8, encoding a protein that belongs to the TCP-1/cpn60 chaperonin family. Proteins in this family are known to play critical roles in protein folding and stress responses, suggesting a potential functional link to plant growth and development(Danisman et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Suzuki et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The identification of a 7-base pair insertion at position 153 of the third exon in HLs represents a significant finding. This insertion introduces a premature termination codon (TAA) at position 50 of the fourth exon, resulting in a truncated protein consisting of only 136 amino acid residues in HLs. Such structural alterations are likely to impair the protein's function, potentially contributing to the observed phenotypic differences in plant height between HLs and SLs. This finding aligns with previous studies demonstrating that premature termination codons often lead to loss-of-function mutations, which can significantly impact agronomic traits(Yang et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe differential expression of \u003cem\u003eGlyma.08g192700\u003c/em\u003e in the SAM during the V2 developmental stage further underscores its functional significance. The markedly elevated expression levels in SLs compared to HLs imply that this gene may function as a negative regulator of PH, with its truncated form in HLs potentially compromising its regulatory capacity. This hypothesis aligns with the established role of TCP family genes in influencing plant architecture and growth(Jone et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; He et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chai et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The integration of genomic and transcriptomic data in this study underscores the efficacy of multi-omics approaches in elucidating complex traits. Through the combination of gene structure analysis, expression profiling, and functional annotation, \u003cem\u003eGlyma.08g192700\u003c/em\u003e has been identified as a strong candidate for \u003cem\u003eqPh08-1\u003c/em\u003e. Nonetheless, further functional validation, such as CRISPR/Cas9-mediated gene editing or transgenic complementation assays, is required to confirm its role in the regulation of PH.\u003c/p\u003e \u003cp\u003eIn summary, this study enhances our comprehension of the genetic mechanisms underlying soybean PH and identifies a valuable candidate gene for molecular breeding. The findings highlight the significance of structural variations within coding regions and their influence on gene function, thereby offering novel insights into the molecular basis of agronomic traits. Future research should investigate the broader regulatory network of \u003cem\u003eGlyma.08g192700\u003c/em\u003e and its interactions with other genes to comprehensively elucidate its role in plant growth and development.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we conducted a comprehensive analysis of the genetic determinants of PH, a vital agronomic trait in soybean, by integrating phenotypic assessments, GWAS, and transcriptomic methodologies. Employing a high-density SNP marker set alongside two robust statistical models, we identified five stable QTNs significantly associated with PH. Within the Gm08_15013896 locus on chromosome 8, we screened 135 candidate genes, and transcriptomic analysis indicated that the expression levels of \u003cem\u003eGlyma.08g192700\u003c/em\u003e were markedly elevated in the SAM at the V2 developmental stage in extremely lines. Through sequence alignment of the coding regions of the gene, \u003cem\u003eGlyma.08g192700\u003c/em\u003e was ultimately identified as the key candidate gene for the \u003cem\u003eqPh08-1\u003c/em\u003e locus. These findings will contribute to the accelerated development of high-yielding soybean varieties with ideal plant architecture, thereby supporting global food security.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Natural Science Foundation of China (32101704), the Major Project of Yancheng Science and Technology (YCBK2024055，YCBK2025070), the National Key Research and Development Program of China (2024YFD1201400)，the Core Technology Development for Breeding Program of Jiangsu Province (JBGS-2021-014), the Major Project of Anhui Provincial Education Department \u0026nbsp;(2023AH040279), the Open fund of Anhui Provincial Key Laboratory for Crop Quality Improvement (2024ZW001), the Coastal Research Foundation Project (YHS202001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e All authors contributed to the study\u0026rsquo;s conception and design. H.Y. and Y.Z. designed the research; H.Y. and S.X. conducted the data analysis, wrote, and revised the manuscript; W.Y. and Q.H. conducted the bioinformatics analysis; H.S., T.Z., Y.Z. and W.Y. modified the manuscript. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e Data are available in the manuscript and in the Supplementary Materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer/Publisher\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003es Note:\u003c/strong\u003e The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of Euphytica and/or the editor(s). Euphytica and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAssefa T, Otyama PI, Brown AV, Kalberer SR, Kulkarni RS, Cannon SB (2019) Genome-wide associations and epistatic interactions for internode number, plant height, seed weight and seed yield in soybean. BMC Genomics 20 (1):527. https://doi.org/10.1186/s12864-019-5907-7\u003c/li\u003e\n \u003cli\u003eBai Z, Ding X, Zhang R, Yang Y, Wei B, Yang S, Gai J (2022) Transcriptome analysis reveals the genes related to pollen abortion in a cytoplasmic male-sterile soybean (\u003cem\u003eGlycine max\u003c/em\u003e (L.) Merr.). 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BMC Plant Biol 23 (1):333. https://doi.org/10.1186/s12870-023-04343-3\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"euphytica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"euph","sideBox":"Learn more about [Euphytica](https://www.springer.com/journal/10681)","snPcode":"10681","submissionUrl":"https://submission.springernature.com/new-submission/10681/3","title":"Euphytica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"soybean, genome-wide association analysis, plant height, transcriptome analysis, candidate genes","lastPublishedDoi":"10.21203/rs.3.rs-9109533/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9109533/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePlant height (PH) is a vital agronomic trait in soybean breeding, exemplifying a classic quantitative trait controlled by multiple genes and significantly influenced by environmental factors. In this study, we undertook an extensive phenotypic assessment of 339 soybean breeding lines adapted to the Yangtze-Huai region of China, evaluated across six different environments over a period of five years. To identify genomic regions associated with PH, we conducted a genome-wide association study (GWAS) utilizing two robust statistical models: the compressed mixed linear model (CMLM) and the Fixed and Random Model Circulating Probability Unification model (FarmCPU). This analysis employed a high-density marker set comprising over 60,000 single nucleotide polymorphism (SNP) markers, facilitating the detection of significant quantitative trait nucleotide (QTN) regions associated with PH. As a result, five stable QTN regions were identified in association with PH. Among these, two regions overlapped with previously reported quantitative trait loci or well-known soybean PH genes, specifically \u003cem\u003eDt1\u003c/em\u003e on and \u003cem\u003eE2\u003c/em\u003e. To identify candidate genes for \u003cem\u003eqPh08-1\u003c/em\u003e loci significantly associated with PH, we identified 135 putative genes within the Gm08_15013896 block. Transcriptome data analysis revealed that the expression levels of \u003cem\u003eGlyma.08g192700\u003c/em\u003e were significantly higher in three extremely short lines (SLs) compared to high lines (HLs) during the V2 developmental stage of the stem apical meristem (SAM) in soybean. DNA sequencing analysis successfully determined the nucleotide sequences of the initial six promoter regions of \u003cem\u003eGlyma.08g192700\u003c/em\u003e. A 7-base pair insertion (TG\u0026rarr;CGCCTGCCG) was identified at position 153 of the third exon, distinguishing HLs from SLs. This insertion introduces a premature termination codon (TAA) at position 50 of the fourth exon, resulting in the HLs variant encoding a truncated protein of only 136 amino acid residues. Therefore, \u003cem\u003eGlyma.08g192700\u003c/em\u003e is likely the most probable gene involved in \u003cem\u003eqPh08-1\u003c/em\u003e.\u003c/p\u003e","manuscriptTitle":"Genome-wide identification and transcriptome analysis revealed candidate genes controlling plant height in soybean using YHSBLP","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 16:40:47","doi":"10.21203/rs.3.rs-9109533/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-09T12:16:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-08T07:36:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T11:07:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300518479618731091065668621875455334378","date":"2026-03-19T00:37:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"56529455517419252059906067929624139067","date":"2026-03-19T00:00:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-16T12:32:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-14T02:06:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-14T02:06:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Euphytica","date":"2026-03-13T02:49:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"euphytica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"euph","sideBox":"Learn more about [Euphytica](https://www.springer.com/journal/10681)","snPcode":"10681","submissionUrl":"https://submission.springernature.com/new-submission/10681/3","title":"Euphytica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7788ea05-714a-496f-beaa-bee3e937c141","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T12:09:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 16:40:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9109533","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9109533","identity":"rs-9109533","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}