Next Article in Journal
Analysis of Semen Proteomic Differences Among Three Genotypes of FecB Rams in Duolang Sheep
Previous Article in Journal
Investigating the “Dark” Genome: First Report of Partington Syndrome in Cyprus
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 16
Issue 10
10.3390/genes16101225
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Integrated GWAS and Transcriptome Analysis to Identify Genes Underlying Plant Height and Ear Height Plasticity in Maize Germplasm

by
Liang Tu
1,†,
Pengfei Liu
1,†,
Angui Wang
1,
Dong Wang
2,
Yunfang Zhu
1,
Gang Li
3,
Yulin Jiang
1,
Xun Wu
1,
Zhiming Zhang
4,
Zehui Chen
1 and
Xiangyang Guo
1,*
1
Institute of Upland Food Crops, Guizhou Academy of Agricultural Sciences, Guiyang 550006, China
2
State Key Laboratory of High-Efciency Production of Wheat-Maize Double Cropping, The Shennong Laboratory, Henan Agricultural University, Zhengzhou 450002, China
3
Biotechnology Research Institute, Fujian Academy of Agricultural Sciences, Fuzhou 350003, China
4
State Key Laboratory of Crop Biology, College of Life Sciences, Shandong Agricultural University, Taian 271018, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Genes 2025, 16(10), 1225; https://doi.org/10.3390/genes16101225
Submission received: 13 September 2025 / Revised: 8 October 2025 / Accepted: 14 October 2025 / Published: 16 October 2025
(This article belongs to the Section Plant Genetics and Genomics)
Browse Figures
Versions Notes

Abstract

Background: Integrating tropical and temperate maize germplasm represents a pivotal strategy for driving genetic innovation, with phenotypic plasticity serving as a critical indicator of successful integration. Methods: We analyzed 155 inbred lines across 5 contrasting environments to investigate variation and plasticity in plant height (PH) and ear height (EH). Results: Genome-wide association studies (GWAS) identified 77 loci associated with phenotypic plasticity, while the transcriptome profiling of Mo17 and T32 in Zhangye and Sanya revealed 9 candidate genes potentially regulating these traits, including Zm00001d043110, Zm00001d053972, and Zm00001d010154. Conclusions: These results provide valuable genetic resources and theoretical foundations for germplasm improvement. The identified genes hold promise for molecular marker development, facilitating the breeding of elite lines with stable PH and EH across environments, thereby contributing to maize improvement and enhancing global food security.

1. Introduction

As one of the world’s most important cereal crops, maize is paramount for ensuring global food security [1]. China has a vast territory with extensive corn-growing regions [2]. Due to the influence of climatic conditions, topographical features, and farming systems, the maize varieties suitable for different ecological regions vary significantly [3]. With the increasing uncertainty of climate change, widespread climate diversity, and extreme weather events triggered by global warming, maize production is facing unprecedented challenges [4,5]. At the same time, issues such as a narrow genetic base of germplasm and a single model of hybrid vigor utilization have become increasingly prominent, restricting innovation in maize breeding and production development. To address these challenges, there is an urgent need to explore new germplasm and establish novel hybrid vigor models [6]. Tropical maize possesses rich genetic diversity, strong resistance, and significant general combining ability (GCA), providing an important resource for expanding the genetic base of maize in China and supporting the breeding of drought-resistant varieties and strong hybrid combinations [7]. However, the strong light cycle sensitivity and vigorous vegetative growth characteristics of tropical maize can lead to prolonged growing periods, excessive plant elongation, and reduced yields in temperate regions, severely limiting its direct application in these areas [8]. Therefore, elucidating the mechanisms of phenotypic plasticity in tropical maize could provide scientific guidance for breeders to precisely improve temperate–tropical maize and develop self-pollinated lines suitable for both northern and southern regions.
While maize is known for its broad ecological adaptability, the introduction of locally adapted varieties into a dissimilar ecological zone can cause pronounced alterations in phenotypic traits such as plant structure and yield, with the potential for considerable financial detriment [9]. For example, when tropical maize is introduced into temperate regions, its plant height (PH) and ear height (EH) may increase significantly, with some plants reaching heights of around 5 m. Moreover, research indicates a significant positive correlation between maize plant height and ear height [10,11]. Building on this, extensive research efforts have been dedicated to studying maize plant architecture within the framework of germplasm utilization and domestication [10,12,13,14].
Phenotypic plasticity refers to the phenomenon where an individual with the same genotype exhibits different phenotypic expressions in different environmental conditions [15,16]. It is a core manifestation of the ecological adaptability of crops [17]. In the improvement of temperate–tropical maize germplasm, reducing phenotypic plasticity is an important goal of artificial selection and a key strategy for developing self-pollinated lines suitable for both northern and southern regions.
Previous studies have demonstrated that crop adaptation is shaped by complex genetic regulatory networks, with key genes playing pivotal roles in this process [18]. Much of the existing research has focused on the genetic and molecular mechanisms underlying crop adaptation, often using flowering time as a primary indicator of phenotypic plasticity [19]. However, examining the environmental adaptation of other traits, particularly PH and EH, is equally important [20]. PH and EH represent some of the most prominent phenotypic differences in tropical maize when grown in temperate regions. Their stability not only reflects the adaptability of the materials but also serves as a key reference for evaluating the success of temperate–tropical maize germplasm improvement. Nonetheless, research on the phenotypic plasticity of PH and EH remains relatively limited.
In this study, a total of 155 inbred lines were selected as experimental materials. These materials were cultivated across five distinct environments: Sanya, Hainan; Guiyang, Guizhou; Shilin, Yunnan; Mojiang, Yunnan; and Zhangye, Gansu, to systematically investigate their phenotypic performance in terms of PH and EH, and the plasticity of these two traits was evaluated. Based on the genotypic data of these inbred lines, a genome-wide association study (GWAS) was performed to identify genetic loci associated with trait plasticity. Furthermore, by integrating transcriptome data of the inbred lines Mo17 and T32 from the Zhangye (Gansu) and Sanya (Hainan) environments, candidate genes controlling the plasticity of PH and EH were further screened. The results of this study will provide valuable genetic resources for developing self-pollinated lines suitable for both northern and southern regions and lay a solid theoretical foundation for further research on the broad adaptability mechanisms of maize.

2. Materials and Methods

2.1. Experimental Materials and Field Assessment

This study used 155 inbred lines as experimental materials, representing a diverse genetic background, including tropical germplasm (e.g., Tuxpeño, Suwan), temperate germplasm (e.g., Lancaster, Iodent), and temperate–tropical improved germplasm.
The panel materials were planted and phenotyped in 2020 at three locations: Guiyang (26.5° N, 106.7° E) in Guizhou Province, Sanya (18.36° N, 109.16° E) in Hainan Province, and Zhangye (38.93° N, 100.45° E) in Gansu Province. In 2022, the trials were conducted in Shilin (24.66° N, 103.38° E) and Mojiang (23.46° N, 101.67° E) in Yunnan Province. Each line was grown in a single 3 m row containing 12 individual plants. Fertilization, irrigation, pest control, and weed management followed local standard field protocols for all trials.

2.2. Genotype Identification

Genotype identification was carried out by the research team in 2019 [21]. The procedure was as follows: These leaves were collected from the experimental materials at the five-leaf stage. Genomic DNA was extracted using a modified CTAB method, and its integrity and concentration were assessed by agarose gel electrophoresis and spectrophotometric analysis. DNA samples that met the quality standards were then sent to Beijing Compson Biotechnology Co., Ltd. (Beijing, China). for subsequent genotype analysis. Genotyping was performed using the Maize SNP 50 chip, which contains 60,000 SNP markers. The SNP data were processed, and high-quality loci were selected according to the method described by Wu et al. [21].

2.3. Quantitative Evaluation of Phenotypic Plasticity

At 15 days after pollination, plant height and ear height of the maize population were measured. Phenotypic plasticity of these traits was assessed using the Finlay–Wilkinson regression model [22]:
Yij = μ + Gi + Ej + βiEj + εij
where Yij is the phenotypic value of the i-th genotype in the j-th environment, μ is the grand mean, Gi and Ej represent the effects of genotype and environment, respectively, and βi is the regression coefficient of the i-th genotype, reflecting its sensitivity to environmental variation and thus its plasticity. The interaction term βiEj captures genotype–environment interactions, while εij denotes residual error.
Quantifying phenotypic plasticity provides a framework to assess the adaptive capacity of maize genotypes. It facilitates the identification of lines with broad environmental adaptability as well as those with environment-specific advantages, thereby informing both ecological studies and breeding strategies.

2.4. Genotype-Phenotype Association Analysis

A total of 40,648 SNPs were employed to perform genome-wide association analyses of phenotypic traits and genotypic variation using the GAPIT package in R (v4.4.0) with the FarmCPU model [23]. A significance threshold of LOD > 3.0 was applied to identify trait-associated loci. This approach ensured that the detected loci represented robust associations, thereby providing a reliable basis for subsequent candidate gene identification and functional analysis.

2.5. Transcriptome Data Analysis

Transcriptome sequencing was conducted as previously described [24]. Two maize inbred lines, T32 and Mo17, were cultivated at two experimental sites, Sanya and Zhangye, which represent the most geographically distant locations in this study and exhibit pronounced differences in climatic conditions. Leaf samples were collected at the V9 growth stage from three biological replicates per line. Total RNA was extracted using TRIzol reagent, and cDNA library preparation and RNA sequencing were performed by Biomarker Technologies (Beijing, China) on the Illumina HiSeq 2000 platform. High-quality clean reads were aligned to the maize B73 reference genome (assembly V4) using TopHat2 [25]. Gene expression levels were quantified as FPKM (fragments per kilobase of transcript per million mapped reads). Differential expression analysis was conducted using the DESeq package in R, with significantly differentially expressed genes identified based on a threshold of Padj < 0.05 and |log2 (fold change)| ≥ 1 [26]. This experimental design enabled the evaluation of transcriptomic responses under contrasting environmental conditions, thereby providing insights into genotype–environment interactions at the molecular level.

2.6. Candidate Gene Prediction

Using the maize B73 reference genome (version V4), genes located within ±100 kb of significant SNPs were designated as candidate genes associated with phenotypic plasticity. Functional annotations of these genes were subsequently retrieved from multiple bioinformatics databases. This approach enables the systematic identification of potential functional genes and provides a foundation for dissecting the molecular mechanisms underlying phenotypic variation.

3. Results

3.1. Plasticity Analysis of PH and EH

Using the Finlay–Wilkinson regression model, we analyzed the plasticity of PH and EH in the panel. The results revealed distinct environmental sensitivity among lines such as LH123, 81162, and Mo17 exhibited relatively low sensitivity of PH and EH to environmental variation, whereas T32, QB2827, and QB2828 displayed pronounced environmental sensitivity. For instance, T32 showed the highest β-PH and β-EH values, 2.90 and 2.55, respectively. As illustrated in Figure 1, the probability density curves of PH and EH plasticity followed a bell-shaped pattern, with low frequencies at both extremes and higher frequencies in the middle, consistent with a normal distribution. The ranges of plasticity values for PH and EH were −0.18~2.90 and −0.14~2.55, respectively. Moreover, the two traits were highly significantly correlated, with a correlation coefficient of 0.598 (Figure 1C).

3.2. GWAS Analysis

We conducted a GWAS for PH and EH plasticity using the FarmCPU model and identified 77 loci significantly associated with trait plasticity (Figure 2; Supplementary Table S1). Of these, 41 loci were linked to PH plasticity (Figure 2A), distributed across chromosome 1 (5 loci), chromosome 2 (5), chromosome 4 (1), chromosome 5 (2), chromosome 7 (1), chromosome 8 (4), chromosome 9 (22), and chromosome 10 (1). In contrast, 35 loci were associated with EH plasticity (Figure 2B), located on chromosome 1 (4 loci), chromosome 3 (11), chromosome 4 (3), chromosome 5 (1), chromosome 6 (4), chromosome 8 (5), chromosome 9 (1), and chromosome 10 (6).
A comparative analysis of the GWAS results revealed a co-localized locus, PZE-108055835, on chromosome 8. Allelic variation analysis indicated a G/A polymorphism at this locus: 107 accessions carried the G allele and 41 carried the A allele. Phenotypic effect analysis further demonstrated that lines with the G allele exhibited reduced plasticity in both PH (β-PH) and EH (β-EH) compared with those carrying the A allele, with mean reductions of 0.22 and 0.11, respectively. The difference in β-PH was statistically significant (p < 0.05) (Figure 3). Collectively, these findings indicate that the A allele represents a superior variant at the PZE-108055835 locus and may serve as a valuable genetic resource for improving phenotypic plasticity and enhancing adaptability in maize breeding programs.

3.3. Integrating GWAS and Transcriptomics to Predict Candidate Genes

By integrating data from the MaizeGDB (https://maizegdb.org, accessed on 13 September 2025) and the maize B73 reference genome (RefGen V4), we identified 164 candidate genes associated with the plasticity of PH and EH (Supplementary Table S2). Transcriptome analysis revealed 17 differentially expressed genes (DEGs) between Mo17 (with lower β-PH and β-EH) and T32 (with higher β-PH and β-EH) in Zhangye, including 10 downregulated and 7 upregulated genes (Figure 4A). In Sanya, 16 DEGs were identified between the two lines, comprising 8 downregulated and 8 upregulated genes (Figure 4C). Comparative expression profiling of the 164 candidate genes across environments showed that T32 exhibited 6 DEGs between Sanya and Zhangye, including 1 downregulated and 5 upregulated genes (Figure 4B). In contrast, Mo17, which maintained relatively stable PH and EH across environments, showed no significant expression changes for 144 genes between the two sites (Figure 4D).
A comparative analysis of candidate genes identified from four contrast groups—“Sanya-Mo17 vs. Zhangye-Mo17 Not significant”, “Zhangye-Mo17 vs. Zhangye-T32 DEGs”, “Sanya-Mo17 vs. Sanya-T32 DEGs”, and “Sanya-T32 vs. Zhangye-T32 DEGs”—was conducted. A Venn diagram (Figure 4E) revealed 9 candidate genes shared among three comparison groups (Table 1). Functional annotation indicated that these genes are involved in critical processes related to plant growth, development, and environmental responses. For instance, Zm00001d043110 was consistently downregulated in Mo17 relative to T32 in both Zhangye and Sanya, whereas T32 displayed upregulation of this gene in Sanya compared with Zhangye. This gene encodes Beta-glucosidase 11 (https://www.uniprot.org, accessed on 13 September 2025), which likely participates in cell wall formation and degradation, hormone activation, and responses to both biotic and abiotic stresses.
An in-depth analysis of the co-localized locus PZE-108055835 identified Zm00001d010154 as a key candidate gene. Transcriptome profiling showed that this gene was differentially expressed between T32 (with higher β-PH and β-EH) and Mo17 (with lower β-PH and β-EH) across both the Sanya and Zhangye environments (Figure 5), highlighting its potential role in regulating phenotypic plasticity.

4. Discussion

Phenotypic plasticity is a critical indicator for evaluating the success of temperate–tropical germplasm integration. In this study, we employed the Finlay–Wilkinson regression model to assess the plasticity of PH and EH in 155 maize inbred lines. The results showed that T32 exhibited high plasticity in both PH and EH, reflecting its strong sensitivity to environmental variation. Previous research has also demonstrated that T32, as a photoperiod-sensitive tropical line, shows substantial environmental responses in traits such as plant architecture and flowering time [24,27,28], further confirming the effectiveness and reliability of the Finlay–Wilkinson model in plasticity analysis. In contrast, Mo17 and other temperate lines displayed relatively stable PH and EH across environments, suggesting reduced sensitivity to environmental fluctuations. This finding highlights the potential of temperate germplasm to address the challenge of excessive PH and EH often observed when tropical germplasm is introduced into temperate regions. Building on this observation, we developed a Suwan–Lancaster population by crossing Mo17 (Lancaster) with Suwan germplasm and subsequently identified a subset of temperate–tropical inbred lines with stable performance for PH and EH across multiple environments [29]. These results not only provide a solid germplasm foundation for improving PH- and EH-related traits but also offer theoretical support for the integration and genetic improvement of temperate–tropical maize germplasm.
The identification of broad-adaptation/phenotypic plasticity genes is of great importance for maize breeding [30,31]. By integrating GWAS and transcriptome data, we predicted nine candidate genes potentially associated with PH and EH plasticity. Functional annotation indicated that these genes play important roles in regulating flowering time, plant height, and stress resistance, highlighting their roles in modulating plant responses to environmental variation. For example, Zm00001d003607 and Zm00001d044542, both encoding F-box domain-containing proteins (https://www.uniprot.org, accessed on 13 September 2025), have been previously reported to regulate flowering time and resistance to banded leaf and sheath blight in maize [32,33], underscoring their importance in environmental response. Notably, the expression of these genes showed no significant differences in Mo17 (with lower β-PH and β-EH) across Zhangye and Sanya; however, both were downregulated in T32 (with higher β-PH and β-EH) relative to Mo17, suggesting their potential roles in regulating PH and EH plasticity.
Similarly, Zm00001d053972 showed stable expression in Mo17 across environments but was significantly upregulated in T32. This gene encodes a DNAJ heat shock family protein (https://www.uniprot.org, accessed on 13 September 2025), a stress-responsive factor widely present in plant cells, which is involved in diverse biotic and abiotic stress responses. Luo et al. [34] reported that such proteins can modulate gibberellin (GA) homeostasis, thereby influencing plant architecture in rice. In addition, we identified a locus, PZE-108055835, that simultaneously regulates PH and EH plasticity, and further pinpointed Zm00001d010154 as its associated candidate gene. Transcriptome analysis revealed significant differential expression of this gene between T32 (with higher β-PH and β-EH) and Mo17 (with lower β-PH and β-EH) across Zhangye and Sanya, indicating that it is also a strong candidate gene underlying plasticity.
The discovery of these candidate genes provides not only theoretical insights into the plasticity of PH and EH but also practical guidance for integrating and improving temperate–tropical maize germplasm. These genes represent promising targets for developing molecular markers, which can facilitate marker-assisted selection (MAS) to identify and breed superior temperate–tropical inbred lines with stable PH and EH across diverse environments. Such advances will accelerate germplasm innovation and contribute to ensuring national food security.

5. Conclusions

In summary, this study establishes phenotypic plasticity as a robust framework for evaluating tropical–temperate maize germplasm integration. By identifying variation across environments and integrating GWAS with transcriptome analyses, we identified 77 loci and 9 candidate genes that deepen our understanding of the genetic basis underlying plant height and ear height plasticity. These findings provide a theoretical foundation for understanding the genetic architecture of phenotypic plasticity and enrich the conceptual basis for germplasm innovation, thereby advancing our knowledge of genotype-by-environmental interactions in shaping complex agronomic traits in maize.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/genes16101225/s1, Table S1: The 77 loci significantly associated with trait plasticity; Table S2: The 164 candidate genes associated with the plasticity of PH and EH.

Author Contributions

Conceptualization, X.G. and Z.C.; Methodology, X.G., Z.C. and Z.Z.; software, L.T., P.L. and D.W.; validation, L.T. and P.L.; formal analysis, D.W. and A.W.; investigation, Y.Z. and L.T.; resources, G.L., Y.J. and X.W.; data curation, L.T. and X.G.; writing—original draft preparation, L.T. and P.L.; writing—review and editing, X.G. and Z.C.; visualization, D.W. and Z.Z.; supervision, X.G.; project administration, X.G.; funding acquisition, X.G.; All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Major projects of agricultural biological breeding [2023ZD0402806]; Youth Science Foundation Project of Guizhou Academy of Agricultural Sciences [QNKYBJJ (2024) 19]; Major scientific and technological achievement transformation projects [Qiankehe Achievements (2024) Major 009]; Guizhou Agricultural Science and Technology Innovation [QNKJCX(2023) 03]; National Natural Science Foundation of China [32160451]; National Key Research and Development Program of China [2023YFD1201103], Guizhou Provincial Science and Technology Plan Project [Qian Kehe Support (2022) key029]; Shandong Provincial Key Research and Development Program (Grant No. 2023TZXD086).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author(s).

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
PHPlant height
EHEar height
GWASGenome-wide association studies
β-PHPlasticity values of plant height
β-EHPlasticity values of ear height
FPKMFragments per kilobase of transcript per million mapped reads
DEGsDifferentially expressed genes
GCASignificant general combining ability

References

  1. Tanumihardjo, S.A.; McCulley, L.; Roh, R.; Lopez-Ridaura, S.; Palacios-Rojas, N.; Gunaratna, N.S. Maize agro-food systems to ensure food and nutrition security in reference to the Sustainable Development Goals. Glob. Food Secur. 2020, 25, 100327. [Google Scholar] [CrossRef]
  2. Peng, Q.; Shen, R.; Li, X.; Ye, T.; Dong, J.; Fu, Y.; Yuan, W. A twenty-year dataset of high-resolution maize distribution in China. Sci. Data 2023, 10, 658. [Google Scholar] [CrossRef]
  3. Zhang, L.; Zhang, Z.; Luo, Y.; Cao, J.; Li, Z. Optimizing genotype-environment-management interactions for maize farmers to adapt to climate change in different agro-ecological zones across China. Sci. Total Environ. 2020, 728, 138614. [Google Scholar] [CrossRef]
  4. Harrington, L.J.; Schleussner, C.F.; Otto, F.E.L. Quantifying uncertainty in aggregated climate change risk assessments. Nat. Commun. 2021, 12, 7140. [Google Scholar] [CrossRef] [PubMed]
  5. Li, F.; Zhou, M.; Shao, J.; Chen, Z.; Wei, X.; Yang, J. Maize, wheat and rice production potential changes in china under the background of climate change. Agric. Syst. 2020, 182, 102853. [Google Scholar] [CrossRef]
  6. Wang, Y.; Zhang, P.; Xiao, Y.; Liu, H.J.; Ding, J.; Qu, Y.; Gao, J.; Li, W.; Guo, X.; Wang, A.; et al. GAIN: A long-term maize germplasm innovation plan. Mol. Plant 2025, 18, 915–919. [Google Scholar] [CrossRef]
  7. Xu, Y.; Skinner, D.J.; Wu, H.; Palacios-Rojas, N.; Araus, J.L.; Yan, J.; Gao, S.; Warburton, M.L.; Crouch, J.H. Advances in maize genomics and their value for enhancing genetic gains from breeding. Int. J. Plant Genom. 2009, 2009, 957602. [Google Scholar] [CrossRef]
  8. Gouesnard, B.; Rebourg, C.; Welcker, C.; Charcosset, A. Analysis of photoperiod sensitivity within a collection of tropical maize populations. Genet. Resour. Crop Evol. 2002, 49, 471–481. [Google Scholar] [CrossRef]
  9. Duvick, D.N. The contribution of breeding to yield advances in maize (Zea mays L.). Adv. Agron. 2005, 86, 83–145. [Google Scholar]
  10. Peiffer, J.A.; Romay, M.C.; Gore, M.A.; Flint-Garcia, S.A.; Zhang, Z.; Millard, M.J.; Gardner, C.A.C.; McMullen, M.D.; Holland, J.B.; Bradbury, P.J.; et al. Genome-wide association study of leaf architecture in the maize nested association mapping population. Nat. Genet. 2014, 46, 108–116. [Google Scholar]
  11. Liu, C.; Zhou, Q.; Dong, L.; Wang, H.; Liu, F.; Weng, J.; Li, X.; Xie, C. Genetic relationship between plant height and ear height in maize (Zea mays L.) and its genetic basis. Crop J. 2021, 9, 1104–1113. [Google Scholar]
  12. Zhao, Y.; Wang, H.; Bo, C.; Dai, W.; Zhang, X.; Li, J.; Dong, H. Genome-wide association study of maize plant architecture using F1 populations. Plant Mol. Biol. 2019, 99, 1–15. [Google Scholar] [CrossRef]
  13. Lan, J.H.; Chu, D. Study on the genetic basis of plant height and ear height in maize (Zea mays L.) by QTL dissection. Yi Chuan 2005, 27, 925–934. [Google Scholar] [PubMed]
  14. Wen, X.; Li, H.Y.; Song, Y.L.; Zhang, P.Y.; Zhang, Z.; Bu, H.H.; Dong, C.L.; Ren, Z.Q.; Chang, J.Z. Genome-wide association study for plant height and ear height in maize under well-watered and water-stressed conditions. BMC Genom. 2025, 26, 745. [Google Scholar] [CrossRef]
  15. Sultan, S.E. Phenotypic plasticity for plant development, function and life history. Trends Plant Sci. 2000, 5, 537–542. [Google Scholar] [CrossRef] [PubMed]
  16. Pigliucci, M. Evolution of phenotypic plasticity: Where are we going now? Trends Ecol. Evol. 2005, 20, 481–486. [Google Scholar] [CrossRef]
  17. Li, P.; An, Z.; Xu, N.; Li, J.; Li, Q.; He, C. Phenotypic plasticity and stability in plants: Genetic mechanisms, environmental adaptation, evolutionary implications, and future directions. Plant Cell Environ. 2025, 48, 5847–5860. [Google Scholar] [CrossRef] [PubMed]
  18. Laitinen, R.A.E.; Nikoloski, Z. Genetic basis of plasticity in plants. J. Exp. 2019, 70, 739–745. [Google Scholar] [CrossRef]
  19. Guo, T.; Mu, Q.; Wang, J.; Vanous, A.E.; Onogi, A.; Iwata, H.; Li, X.; Yu, J. Dynamic effects of interacting genes underlying rice flowering-time phenotypic plasticity and global adaptation. Genome Res. 2020, 30, 673–683. [Google Scholar] [CrossRef]
  20. Ma, Y. Genetic Dissection and Candidate Mining of Theplasticity of Maize Plant Height and Flowering Time. Ph.D. Thesis, Shenyang Agricultural University, Shenyang, China, 2024; pp. 1–121. [Google Scholar]
  21. Wu, X.; Wang, A.; Guo, X.; Liu, P.; Zhu, Y.; Li, X.; Chen, Z. Genetic characterization of maize germplasm derived from Suwan population and temperate resources. Hereditas 2019, 156, 2. [Google Scholar] [CrossRef]
  22. Finlay, K.W.; Wilkinson, G.N. The analysis of adaptation in a plant-breeding programme. Crop Pasture Sci. 1963, 14, 742–754. [Google Scholar] [CrossRef]
  23. Kaler, A.S.; Gillman, J.D.; Beissinger, T.; Purcell, L.C. Comparing different statistical models and multiple testing corrections for association mapping in soybean and maize. Front. Plant Sci. 2020, 10, 1794. [Google Scholar] [CrossRef]
  24. Wu, X.; Liu, Y.; Lu, X.; Tu, L.; Gao, Y.; Wang, D.; Guo, S.; Xiao, Y.; Xiao, P.; Guo, X.; et al. Integration of GWAS, linkage analysis and transcriptome analysis to reveal the genetic basis of flowering time-related traits in maize. Front. Plant Sci. 2023, 14, 1145327. [Google Scholar] [CrossRef] [PubMed]
  25. Trapnell, C.; Roberts, A.; Goff, L.; Pertea, G.; Kim, D.; Kelley, D.R.; Pimentel, H.; Salzberg, S.L.; Rinn, J.L.; Pachter, L. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 2012, 7, 562–578. [Google Scholar] [CrossRef] [PubMed]
  26. Anders, S.; Huber, W. Differential expression analysis for sequence count data. Genome Biol. 2010, 11, R106. [Google Scholar] [CrossRef]
  27. Jiang, Y.; Guo, S.; Wang, D.; Tu, L.; Liu, P.; Guo, X.; Wang, A.; Zhu, Y.; Lu, X.; Chen, Z.; et al. Integrated GWAS, linkage, and transcriptome analysis to identify genetic loci and candidate genes for photoperiod sensitivity in maize. Front. Plant Sci. 2024, 15, 1441288. [Google Scholar] [CrossRef]
  28. Lu, X.; Liu, P.; Tu, L.; Guo, X.; Wang, A.; Zhu, Y.; Jiang, Y.; Zhang, C.; Xu, Y.; Chen, Z.; et al. Joint-GWAS, linkage mapping, and transcriptome analysis to reveal the genetic basis of plant architecture-related traits in maize. Int. J. Mol. Sci. 2024, 25, 2694. [Google Scholar] [CrossRef]
  29. Zhu, Y.; Chen, Z.; Guo, X.; Wang, A.; Wu, X.; Liu, P. Breeding and application of inbred line QB446 based on Suwan-Lancaster population improvement of maize. Guizhou Agric. Sci. 2020, 48, 1–5. [Google Scholar]
  30. Liu, N.; Du, Y.; Warburton, M.L.; Xiao, Y.; Yan, J. Phenotypic Plasticity Contributes to Maize Adaptation and Heterosis. Mol. Biol. Evol. 2021, 38, 1262–1275. [Google Scholar] [CrossRef]
  31. Jin, M.; Liu, H.; Liu, X.; Guo, T.; Guo, J.; Yin, Y.; Ji, Y.; Li, Z.; Zhang, J.; Wang, X.; et al. Complex genetic architecture underlying the plasticity of maize agronomic traits. Plant Commun. 2023, 4, 100473. [Google Scholar] [CrossRef]
  32. Rieu, P.; Turchi, L.; Thévenon, E.; Zarkadas, E.; Nanao, M.; Chahtane, H.; Tichtinsky, G.; Lucas, J.; Blanc-Mathieu, R.; Zubieta, C.; et al. The F-box protein UFO controls flower development by redirecting the master transcription factor LEAFY to new cis-elements. Nat. Plants 2023, 9, 315–329. [Google Scholar] [CrossRef] [PubMed]
  33. Li, N.; Lin, B.; Wang, H.; Li, X.; Yang, F.; Ding, X.; Yan, J.; Chu, Z. Natural variation in ZmFBL41 confers banded leaf and sheath blight resistance in maize. Nat. Genet. 2019, 51, 1540–1548. [Google Scholar] [CrossRef] [PubMed]
  34. Luo, L.; Xie, Y.; Yu, S.; Yang, J.; Chen, S.; Yuan, X.; Guo, T.; Wang, H.; Liu, Y.; Chen, C.; et al. The DnaJ domain-containing heat-shock protein NAL11 determines plant architecture by mediating gibberellin homeostasis in rice (Oryza sativa). New Phytol. 2023, 237, 2163–2179. [Google Scholar] [CrossRef] [PubMed]
Genes 16 01225 g001
Figure 1. Normal distribution and correlation analysis of plasticity in PH and EH across the population. (A) Phenotypic plasticity for plant height (β-PH) exhibited an approximately normal distribution; (B) Phenotypic plasticity for ear height (β-EH) followed a near-normal distribution; (C) Correlation analysis between plant height and ear height phenotypic plasticity revealed a statistically significant relationship (p < 0.001). The dashed lines in (A) and (B) represent the theoretical normal distribution curves, while the solid lines depict the normal distribution curves fitted to the empirical data. The red solid line in (C) indicates the correlation between β-PH and β-EH.
Figure 1. Normal distribution and correlation analysis of plasticity in PH and EH across the population. (A) Phenotypic plasticity for plant height (β-PH) exhibited an approximately normal distribution; (B) Phenotypic plasticity for ear height (β-EH) followed a near-normal distribution; (C) Correlation analysis between plant height and ear height phenotypic plasticity revealed a statistically significant relationship (p < 0.001). The dashed lines in (A) and (B) represent the theoretical normal distribution curves, while the solid lines depict the normal distribution curves fitted to the empirical data. The red solid line in (C) indicates the correlation between β-PH and β-EH.
Genes 16 01225 g001
Genes 16 01225 g002
Figure 2. GWAS results for plasticity of PH and EH. (A) indicates loci with LOD > 3.0 for phenotypic plasticity of plant height (β-PH) in the GWAS results; (B) indicates loci with LOD > 3.0 for phenotypic plasticity of ear height (β-EH) in the GWAS results; the vertical dashed line signifies the co-localization of both traits at the PZE-108055835 locus.
Figure 2. GWAS results for plasticity of PH and EH. (A) indicates loci with LOD > 3.0 for phenotypic plasticity of plant height (β-PH) in the GWAS results; (B) indicates loci with LOD > 3.0 for phenotypic plasticity of ear height (β-EH) in the GWAS results; the vertical dashed line signifies the co-localization of both traits at the PZE-108055835 locus.
Genes 16 01225 g002
Genes 16 01225 g003
Figure 3. Superior allelic variant at the PZE-108055835. (A) illustrates the allelic variation in β-PH at locus PZE-108055835, showcasing a significant difference; (B) shows the allelic variation in β-EH at the same locus, which was not statistically significant (*, Student’s t-test, p < 0.05).
Figure 3. Superior allelic variant at the PZE-108055835. (A) illustrates the allelic variation in β-PH at locus PZE-108055835, showcasing a significant difference; (B) shows the allelic variation in β-EH at the same locus, which was not statistically significant (*, Student’s t-test, p < 0.05).
Genes 16 01225 g003
Genes 16 01225 g004
Figure 4. Expression patterns of 164 candidate genes in Mo17 and T32 across Zhangye and Sanya. (A) Volcano plot of Zhangye-Mo17 vs. Zhangye-T32, Blue represents upregulated genes and red represents downregulated genes. (B) Volcano plot of Sanya-T32 vs. Zhangye-T32, Blue represents upregulated genes and red represents downregulated genes. (C) Volcano plot of Sanya-Mo17 vs. Sanya-T32, Blue represents upregulated genes and red represents downregulated genes. (D) Volcano plot of Sanya-Mo17 vs. Zhangye-Mo17, Red indicates genes with no significant difference. (E) Venn diagram of “Sanya-Mo17 vs. Zhangye-Mo17 Not significant”, “Zhangye-Mo17 vs. Zhangye-T32 DEGs”, “Sanya-Mo17 vs. Sanya-T32 DEGs,” and “Sanya-T32 vs. Zhangye-T32 DEGs.”.
Figure 4. Expression patterns of 164 candidate genes in Mo17 and T32 across Zhangye and Sanya. (A) Volcano plot of Zhangye-Mo17 vs. Zhangye-T32, Blue represents upregulated genes and red represents downregulated genes. (B) Volcano plot of Sanya-T32 vs. Zhangye-T32, Blue represents upregulated genes and red represents downregulated genes. (C) Volcano plot of Sanya-Mo17 vs. Sanya-T32, Blue represents upregulated genes and red represents downregulated genes. (D) Volcano plot of Sanya-Mo17 vs. Zhangye-Mo17, Red indicates genes with no significant difference. (E) Venn diagram of “Sanya-Mo17 vs. Zhangye-Mo17 Not significant”, “Zhangye-Mo17 vs. Zhangye-T32 DEGs”, “Sanya-Mo17 vs. Sanya-T32 DEGs,” and “Sanya-T32 vs. Zhangye-T32 DEGs.”.
Genes 16 01225 g004
Genes 16 01225 g005
Figure 5. Relative expression of Zm00001d010154 across environments. (A) Differences in β-PH (left) and β-EH (right) between T32 and Mo17; (B) Relative expression level of Zm00001d010154 in T32 and Mo17 under Zhangye (left) and Sanya (right) environments; *, Student’s t-test, p < 0.05.
Figure 5. Relative expression of Zm00001d010154 across environments. (A) Differences in β-PH (left) and β-EH (right) between T32 and Mo17; (B) Relative expression level of Zm00001d010154 in T32 and Mo17 under Zhangye (left) and Sanya (right) environments; *, Student’s t-test, p < 0.05.
Genes 16 01225 g005
Table 1. Candidate genes identified by integrating GWAS and transcriptome analysis.
Table 1. Candidate genes identified by integrating GWAS and transcriptome analysis.
GeneComparisonDifference ExpressedFunction
Zm00001d003607Sanya-Mo17 vs. Zhangye-Mo17NoF-box domain-containing protein
Zhangye-Mo17 vs. Zhangye-T32Down
Sanya-Mo17 vs. Sanya-T32Down
Zm00001d023682Sanya-Mo17 vs. Zhangye-Mo17NoABC transporter F family member 1
Zhangye-Mo17 vs. Zhangye-T32Up
Sanya-Mo17 vs. Sanya-T32Up
Zm00001d026647Sanya-Mo17 vs. Zhangye-Mo17NoDNA mismatch repair protein MSH3
Zhangye-Mo17 vs. Zhangye-T32Down
Sanya-Mo17 vs. Sanya-T32Down
Zm00001d026651Sanya-Mo17 vs. Zhangye-Mo17NoExpressed protein%3B protein
Zhangye-Mo17 vs. Zhangye-T32Up
Sanya-Mo17 vs. Sanya-T32Up
Zm00001d043110Zhangye-Mo17 vs. Zhangye-T32DownBeta-glucosidase 11
Sanya-Mo17 vs. Sanya-T32Down
Sanya-T32 vs. Zhangye-T32Up
Zm00001d044542Sanya-Mo17 vs. Zhangye-Mo17NoF-box domain-containing protein
Zhangye-Mo17 vs. Zhangye-T32Down
Sanya-Mo17 vs. Sanya-T32Down
Zm00001d045384Sanya-Mo17 vs. Zhangye-Mo17NoSuperoxide dismutase16
Zhangye-Mo17 vs. Zhangye-T32Down
Sanya-Mo17 vs. Sanya-T32Down
Zm00001d045386Sanya-Mo17 vs. Zhangye-Mo17NoEthylene-responsive transcription factor RAP2-2
Zhangye-Mo17 vs. Zhangye-T32Down
Sanya-Mo17 vs. Sanya-T32Down
Zm00001d053972Sanya-Mo17 vs. Zhangye-Mo17NoDNAJ heat shock family protein
Zhangye-Mo17 vs. Zhangye-T32Up
Sanya-Mo17 vs. Sanya-T32Up
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 MDPI and/or the editor(s). MDPI 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.

Share and Cite

MDPI and ACS Style

Tu, L.; Liu, P.; Wang, A.; Wang, D.; Zhu, Y.; Li, G.; Jiang, Y.; Wu, X.; Zhang, Z.; Chen, Z.; et al. Integrated GWAS and Transcriptome Analysis to Identify Genes Underlying Plant Height and Ear Height Plasticity in Maize Germplasm. Genes 2025, 16, 1225. https://doi.org/10.3390/genes16101225

AMA Style

Tu L, Liu P, Wang A, Wang D, Zhu Y, Li G, Jiang Y, Wu X, Zhang Z, Chen Z, et al. Integrated GWAS and Transcriptome Analysis to Identify Genes Underlying Plant Height and Ear Height Plasticity in Maize Germplasm. Genes. 2025; 16(10):1225. https://doi.org/10.3390/genes16101225

Chicago/Turabian Style

Tu, Liang, Pengfei Liu, Angui Wang, Dong Wang, Yunfang Zhu, Gang Li, Yulin Jiang, Xun Wu, Zhiming Zhang, Zehui Chen, and et al. 2025. "Integrated GWAS and Transcriptome Analysis to Identify Genes Underlying Plant Height and Ear Height Plasticity in Maize Germplasm" Genes 16, no. 10: 1225. https://doi.org/10.3390/genes16101225

APA Style

Tu, L., Liu, P., Wang, A., Wang, D., Zhu, Y., Li, G., Jiang, Y., Wu, X., Zhang, Z., Chen, Z., & Guo, X. (2025). Integrated GWAS and Transcriptome Analysis to Identify Genes Underlying Plant Height and Ear Height Plasticity in Maize Germplasm. Genes, 16(10), 1225. https://doi.org/10.3390/genes16101225

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop