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Article

QTL Mapping and Candidate Gene Screening for Enhancing Oil Content in Silage Maize

by
Jianzhong Wu
1,†,
Qi Wang
2,†,
Weibo Han
1,
Qian Zhao
3,
Dequan Sun
1,* and
Zhongbao Shen
1,*
1
Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China
2
College of Life Science and Technology, Harbin Normal University, Harbin 150025, China
3
Cultivation and Farming Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Plants 2025, 14(8), 1181; https://doi.org/10.3390/plants14081181
Submission received: 9 March 2025 / Revised: 8 April 2025 / Accepted: 9 April 2025 / Published: 10 April 2025
(This article belongs to the Special Issue Bioinformatics and Functional Genomics in Modern Plant Science)
Browse Figures
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Abstract

:
Assessing the nutritional quality of silage maize (Zea mays L.) hinges largely on its oil content, a complex quantitative trait influenced by multiple genes. Mining candidate genes within oil content-related quantitative trait loci (QTLs) can provide genetic resources and a theoretical foundation for cultivating high-oil silage maize varieties. This study employed 274 doubled haploid (DH) lines derived from the parental lines BY4944 and DNF34-2 to perform main gene plus polygene mixed genetic analysis and complex interval mapping (CIM), with the goal of pinpointing oil content-related QTLs and genes distributed across the Z. mays L. genome. Leveraging 5400 single nucleotide polymorphism (SNPs), a high-resolution silage maize genetic linkage map covering 3864.51 cM was constructed with an average interval between markers of 0.74 cM. Analysis of the map revealed 13 oil content-related QTLs. The most significant large-effect QTL (qOIL-1-1), located on chromosome 1 within the region spanning 240.93 Mb to 256.57 Mb, exhibited a logarithm of odds (LOD) score of 3.34 and explained 5.06% of oil content-related phenotypic variation. Within these QTLs, 617 genes were annotated. Through transcriptome analysis combined with quantitative real-time polymerase chain reaction (RT-qPCR), five candidate genes potentially associated with oil content were predicted and subsequently validated within these genetic loci. This research underscores the potential of identifying candidate genes to enhance breeding efforts aimed at augmenting oil content, thereby advancing animal husbandry practices.

1. Introduction

Corn silage (Zea mays L.) is renowned for its high nutritional value, palatability, and digestibility, serving as a staple component of livestock feed. Its global cultivation area ranks consistently among the top three forages worldwide [1]. Its proven ability to enhance both livestock production efficiency and quality underscores its pivotal role in driving economic prosperity within the industry [2]. With the rapid expansion of animal husbandry and the dairy sector, there is a pressing need to further enhance the nutritional attributes of corn silage to meet the escalating demand for high-quality feed.
Oil content plays vital roles in various biological processes associated with plant growth and development. During the ensiling process of high-oil maize, specific lipid constituents undergo enzymatic conversion into nutritionally advanced fatty acids. These bioactive compounds enhance livestock and poultry health indices, including growth performance parameters (body weight gain, feed efficiency) and developmental biomarkers, while concurrently improving the nutritional profile of livestock products [3]. Compared to conventional corn, high-oil corn exhibits superior energy density and nutritional composition, which enables reduced daily feed consumption when utilized as animal feedstock [4]. Furthermore, high-oil corn varieties demonstrate desirable agronomic characteristics, including enhanced plant vigor, elevated biomass production, and robust stress tolerance [5].
Quantitative trait loci (QTL) mapping serves as the cornerstone of molecular breeding and plays a pivotal role in facilitating and accelerating the development of improved crops. The identification of chromosomal regions associated with oil content-related traits through QTL mapping contributes to the development of maize germplasm with diverse genetic backgrounds. For example, Yang et al. [6] identified 42 QTLs located in 21 genomic regions governing fatty acid composition and grain oil content that explained between 0.7 and 48.3% of the phenotypic variation in these traits, respectively. In addition, Lin et al. [7] identified 70 QTL clusters related to corn kernel oil content spanning 34.5% of the maize genome, with 59 (84%) QTL clusters co-isolated with at least one gene within the set of 147 oil-related genes. Furthermore, Fang et al. [8] detected a total of 62 QTLs related to oil content traits, with contribution rates ranging from 12.5% to 52.5%, while Yang [9] et al. detected a total of 11 oil-related QTLs, including a single QTL explaining 4.6% to 11.1% of the phenotypic variation in this trait. In a separate study, Yu et al. [10] detected a total of nine oil content-related conditional QTLs with contribution rates ranging from 2.4 to 20.6%.
Innovations in genomic technologies have propelled quantitative trait genetic dissection into a new era, where high-density molecular markers combined with genome-wide association-based QTL mapping approaches have emerged as the central strategy for dissecting the genetic basis of complex agronomic traits [11]. Integrated QTL–transcriptome analysis can reduce the candidate gene screening scope by 75–90%, thereby significantly enhancing functional validation success rates, as evidenced in crops such as cotton [12], maize [13], and rapeseed [14].
Studies have shown that in corn, a low-oil seed crop, only 20.1% of oil-related genes are regulated. Moreover, the corn genome contains fewer oil biosynthesis genes compared to genomes of high-oil seeds [15]. Notably, while many maize genes share homology with regulated genes in other seed oil crops, most maize genes encoding oil-associated transcription factors, triacylglycerol (TAG) biosynthetase, pentose phosphate pathway (PPP), and Calvin cycle proteins are not regulated during seed oil synthesis [16]. These findings underscore the intricate genetic regulation underlying maize oil content and emphasize the critical importance of developing silage-specific high-oil corn hybrids for advancing livestock industry development.
Herein, we established a population of 274 double haploid (DH) lines derived from parental lines (BY4944 and DNF34-2) exhibiting significant oil content divergence. A high-density genetic linkage map was constructed using composite interval mapping (CIM) to identify significant QTLs. Candidate genes associated with oil content were predicted and validated through transcriptome analysis combined with RT-qPCR. This study provides a theoretical foundation for elucidating the genetic basis of oil content in silage maize and guides the targeted selection of elite germplasms for developing varieties with high-oil kernels.

2. Results

2.1. Analysis of Silage Maize Grain Phenotypic Variation

The mean oil content of the DH population was 5.72, with a standard deviation of 1.07 and an environmental variance of 0.14. The coefficient of variation was calculated at 18.25%, indicating significant potential for genetic enhancement within this trait. Subsequent Kolmogorov–Smirnov (K-S) testing revealed a Sig value (p-value) for oil content exceeding 0.05 (Table 1), indicating conformity with a normal distribution pattern (Figure 1).
Phenotypic values of the parents were found at the two extremes of the population distribution. Notably, the average phenotypic value of the DH population was lower than that of the median parent, indicating a negative transgressive inheritance. However, the oil content traits displayed a tendency to align more closely with those of the male parent.

2.2. Sequencing and SNP Identification

The raw reads obtained from the 274 DH lines exhibited substantial variation, ranging from 60.25 Mb to 772.37 Mb, with a mean of 449.63 Mb. The 1× genome coverage spanned 82.02%~91.94% (mean 90.03%), while 4× genome coverage ranged between 81.93~94.22% (mean 88.56%). The parental lines BY4944 and DNF34-2 generated 376.35 Mb and 398.73 Mb raw reads, respectively, demonstrating 1× coverage of 96.95% and 97.21%, and 4× coverage reaching 94.27% and 96.11%, correspondingly. After filtering, the clean reads from the DH population exhibited genome coverage ranging from 54.37 Mb to 706.31 Mb, with an average coverage of 413.82 Mb; the genome coverage values of clean reads from the BY4944 and DNF34-2 parental lines were 348.75 Mb and 371.53 Mb, respectively. The Q20 values for the clean reads of the DH population ranged from 97.21% to 99.13%, with an average value of 98.38%; the Q20 values of BY4944 and DNF34-2 were 99.27% and 99.20%, respectively (Table S1).
A total of 51,943 polymorphic SNP molecular markers were identified, with 14,510 of them belonging to type aa × bb. After progeny polymorphism filtering, 5400 SNPs distributed across the ten maize chromosomes were identified, with SNP numbers per chromosome ranging from 302 to 1014 (Table 2, Figure S1). The genome coverage of the DH population of SNPs ranged from 82.02% to 91.94% (mean 90.03%), while that of the BY4944 and DNF34-2 parental lines covered 93.12% and 93.24% of the genome, respectively (Table S1). Core SNP sites were mainly found within exonic (29.16%), intronic (24.94%), and intergenic (15.3%) regions (Figure 2).

2.3. Construction of Genetic Linkage Map

We successfully constructed a genetic linkage map comprising 10 linkage groups (LG01-LG10), with a total length of 3864.51 cM (Table 2, Figure 3). The genetic distances between the 10 linkage groups ranged from 255.91 cM to 627.74 cM, with the average distances between markers ranging from 0.49 cM to 0.95 cM. Notably, the maximum distance between markers within LG03 was 42.76 cM and between markers within LG10 was 13.06 cM. Furthermore, we observed consistent collinearity between the 5400 markers and their physical locations in the reference genome (Figure S2), confirming the high accuracy and reliability of our constructed genetic linkage map.

2.4. Mapping of Silage Maize QTLs

In this study, we identified 13 QTLs associated with oil content distributed across chromosomes 1, 3, 4, 5, 6, and 8, respectively (Table 3). The LOD scores ranged from 2.59 to 5.15, indicating the high statistical significance of these QTLs (Figure 4), and the contribution rates indicating explained phenotype variation rates ranged from 0.01% to 5.06%.
Among these QTLs, qOIL-1-1 emerged as noteworthy, with an LOD value of 3.34 and a physical interval spanning 240.93 to 256.57 Mb on chromosome 1. This QTL accounted for the largest effect on oil content, explaining 5.06% of the phenotypic variation in this trait. Therefore, this QTL was hypothesized to be a main effect QTL, with additive effect values ranging from −0.25 to −0.22.

2.5. Prediction of Candidate Genes Within QTL Intervals Associated with Silage Maize Oil Content Traits

The correlation coefficients (γ) among biological replicates within both the OH and OL groups exceeded 0.9, indicating strong inter-sample consistency and experimental reproducibility (Figure S3). Comparative transcriptomic analysis identified 981 differentially expressed genes (DEGs), with 549 upregulated and 432 downregulated in OH compared to OL (Figure 5A). Gene Ontology (GO) enrichment revealed 617 DEGs (p < 0.05) significantly clustered into 21 subcategories across three major domains: biological process (BP), cellular component (CC), and molecular function (MF). BP dominated the functional annotations, encompassing the highest proportion of terms, including RNA biosynthetic process (GO:0032774), inositol phosphate biosynthetic process (GO:0010919), and regulation of lipase activity (GO:0060191). CC annotations highlighted plasma membrane (GO:0005887), organelle inner membrane (GO:0031226), and cytoplasmic vesicle (GO:0031410), while MF terms featured ligase activity (GO:0016874), α-1,2-mannosyltransferase activity (GO:0000026), and geranylgeranyltransferase activity (GO:0004337) (Figure 5B).
KEGG pathway analysis mapped all differentially expressed genes (DEGs) to 113 metabolic pathways (Figure 5C). The top 20 enriched pathways included ether lipid metabolism (ko00565), diterpenoid biosynthesis (ko00904), and critical lipid-related pathways such as α-linolenic acid metabolism (ko00592) and glycerophospholipid metabolism (ko00564). Redundancy filtering identified 23 unique DEGs functionally linked to these lipid synthesis pathways.
To refine candidate selection, 232 significantly differential genes were screened from QTL-associated linkage groups. Physical alignment of these genes with QTL intervals identified five oil biosynthesis candidates (Table 4): GRMZM2G133398 and GRMZM2G156861 within qOIL-3-2, GRMZM2G125268 (qOIL-4-2), GRMZM2G002959 (qOIL-6-2), and GRMZM2G343588 (qOIL-8-1).

2.6. Validation of Oil Content-Associated Differentially Expressed Genes via RT-qPCR

Utilizing maize ACT (J01238.1) as the internal reference gene, the expression patterns of candidate genes were examined in OH, OL, and parental lines. As depicted in Figure 6, the five genes displayed distinct expression levels across the samples, yet their expression trends were consistent with the transcriptome sequencing results, thereby confirming the reliability of the screening outcomes. GRMZM2G156861 and GRMZM2G343588 exhibited negative regulatory roles in maize oil content, while GRMZM2G133398, GRMZM2G125268, and GRMZM2G002959 functioned as positive regulators of this trait.

3. Discussion

3.1. Utilizing Genetic Modeling in Silage Maize Breeding

Conventional breeding methods have consistently proven to be labor-intensive and cost-prohibitive. However, the utilization of doubled haploid (DH) populations demonstrates significant advantages in mitigating complexities associated with heterozygous loci through dominance and non-epistatic effects, thereby minimizing environmental variance interference [17]. Concurrently, DH-based breeding technology accelerates the attainment of homozygosity and substantially reduces the duration of the breeding cycle [18].
In this investigation, we examined the phenotypic variation and genetic architecture of oil content traits by constructing a doubled haploid (DH) population comprising 274 lines derived from two distinctly divergent parental inbred lines, BY4944 and DNF34-2. Our analysis revealed that the offspring oil content traits exhibited a normal distribution pattern, which is highly advantageous for quantitative trait loci (QTL) mapping.

3.2. High-Density Genetic Mapping and QTL Analysis

The construction of a genetic linkage map is a foundational prerequisite for deciphering the maize genome and identifying genes that govern oil content-related traits [18]. To ensure the precision of genetic mapping, we implemented a rigorous screening of progeny sequences through Genotyping-by-Targeted Sequencing (GBTS) to obtain high-fidelity molecular markers.
Ultimately, 5400 markers were obtained that were distributed among ten linkage groups, with the average interval between adjacent markers ranging from 0.49 cM to 0.95 cM, indicating high linkage map resolution. Nonetheless, notable disparities in genetic distances and considerable variability in numbers of molecular markers within the linkage groups were observed. This phenomenon stemmed from the heterogeneous distribution of SNP molecules across the chromosomes of the parental lines, culminating in variations in the reassortment rates among the hybrid offspring, thereby leading to divergent marker counts within each linked group [19].
Subsequent QTL analysis of the high-density genetic maps in conjunction with phenotypic data detected a total of 13 QTLs that were intricately linked to oil content traits that collectively accounted for phenotypic variations ranging from 0.01% to 5.06%. Notably, qOIL-1-1, situated on chromosome 1 within the region spanning 240.93 Mb to 256.57 Mb, emerged as a pivotal candidate. With a substantial LOD value of 3.34, qOIL-1-1 explained 5.06% of the phenotypic variation in oil content, the largest effect observed, thereby signifying its important role as a main effect QTL and highlighting its potential applicability for marker-assisted selection strategies.
Previous studies have demonstrated the presence of oil content-regulating genes across all 10 maize chromosomes [6,7,8,9], prompting comparisons between these QTLs and those identified in the current study. For example, Yang et al. [6] identified 42 maize QTLs governing fatty acid composition and grain oil content across all 10 chromosomes, including notable QTLs such as lin4 (PVE: 4%) on chromosome 4, which is close to qOIL-4-1 identified herein, and ste6-2 (PVE: 9.3%) on chromosome 6, which is close to qOIL-6-1 identified herein. Additionally, QTLs identified by Yang et al. [9] on chromosomes 1, 4, 5, 6, 7, 8, and 10 included qOIL06-02 (PVE: 6.4%) on chromosome 6, which is relatively close to (and possibly identical to) qOIL-6-1 identified herein. Moreover, Yu et al. [10] identified nine unconditional QTLs on chromosomes 1, 2, 4, 5, 6, 8, 9, and 10 associated with oil content, including oilc4-1 (PVE: 2.6%) located on chromosome 4 close to (and possibly identical to) qOIL-4-3 identified in this study. It is noteworthy that although oilc4-1 is associated with oil content and is located close to qOIL-4-2, it is conditioned by starch content. These regions within chromosome 4 evidently exert substantial influence on oil content regulation, as evidenced by the consistent detection of stable QTLs across diverse genetic backgrounds. Such insights hold promise for fine QTL mapping endeavors and candidate gene identification in subsequent investigations. Furthermore, it is plausible that unreported QTLs might exhibit population-specific effects influenced by distinct genetic backgrounds [20].
The novel QTLs identified in this investigation complement prior research findings. Notably, qOIL-4-2 overlaps with qOIL-4-3, and qOIL-8-1 overlaps with qOIL-8-2, signifying potential interplay among QTLs within close chromosomal proximity. This phenomenon of clustered distribution underscores the multifaceted nature of QTL localization, wherein multiple traits may be influenced by one or more QTLs situated in parallel or analogous locations [21]. Such observations enrich our understanding of the complex genetic architecture governing maize oil content and pave the way for elucidating intricate regulatory mechanisms underlying this economically significant trait.

3.3. Screening of Candidate Genes

This study conducted GO enrichment analysis on differentially expressed genes related to oil content, identifying GO terms directly associated with lipid biosynthesis and metabolism, as well as the indirect functional roles of oil-associated genes. Among the biological process and molecular function categories, the most enriched subterms were RNA biosynthetic process and ligase activity, respectively. Previous research has demonstrated that lipids can directly regulate RNA activity in a simplified synthetic system [22] and modulate ubiquitin ligase activity [23]. Our findings indicate that oil-related differentially expressed genes have regulatory impacts on RNA biosynthetic processes and ligase activity at the transcriptional level. Specifically, α-1,2-mannosyltransferase plays a role in lipid-linked oligosaccharide assembly [24], while geranylgeranyl transferase requires knockdown to activate key regulators of membrane protein endocytic transport and subcellular trafficking, a method commonly used to investigate lipid homeostasis [25]. These results suggest that oil-regulatory genes influence enzymatic activities, warranting further investigation to validate their mechanistic coupling with lipid functionality. KEGG pathway analysis further delineated the biological roles and metabolic pathways of oil content-associated genes, including direct involvement in ether lipid metabolism [26], diterpenoid biosynthesis [27], vitamin B6 metabolism [28], linoleic acid metabolism [29], steroid biosynthesis [30], α-linolenic acid metabolism [31], citrate cycle (TCA cycle) [32], pantothenate and CoA biosynthesis [33], glycerophospholipid metabolism [34], and propanoate metabolism [35]. Moreover, although previous studies have indicated positive correlations between maize kernel oil content and levels of protein, tryptophan, and lysine, our data reveal a significant enrichment of oil-related genes within the metabolic pathways of valine, leucine, isoleucine, serine, threonine, and glycine.
We identified five candidate genes that regulate the oil content in maize. GRMZM2G133398, homologous to Arabidopsis AT3G24650 (annotated as an AP2/B3-like transcription factor family protein, encoding ABA-INSENSITIVE 3 [ABI3]), was previously believed to influence lipid accumulation indirectly via FUSCA3 (FUS3). However, Zheng et al. [36] demonstrated that ABI3 independently activates lipid biosynthesis. GRMZM2G125268, homologous to Arabidopsis AT3G48000 (which encodes mitochondrial aldehyde dehydrogenase RF2B), oxidizes short-chain fatty aldehydes to produce fatty acids with one fewer carbon atom, as reported by Liu et al. [37] in maize. GRMZM2G343588, homologous to Arabidopsis AT3G16785 (which encodes phospholipase D1), catalyzes the conversion of diacylglycerol (DAG) to triacylglycerols (TAG) [38,39]. These three genes are believed to act as positive regulators of oil accumulation. In contrast, GRMZM2G156861, homologous to Arabidopsis AT1G55020 (encoding lipoxygenase 1 [LOX1]), promotes fatty acid oxidation and alters lipid metabolism [40], while GRMZM2G002959, homologous to Arabidopsis AT3G51840 (encoding acyl-CoA oxidase 4), catabolizes long-chain fatty acids [41]. These two genes may act as negative regulators during oil biosynthesis.
The mechanistic roles of these candidate genes in maize lipid synthesis and metabolism have yet to be fully elucidated. Future studies should prioritize comprehensive screening and validation of target genes using advanced methodologies, including genome-wide association analysis, QTL fine mapping, and molecular marker-assisted breeding. Systematic functional characterization—such as CRISPR-based gene editing, lipidomic profiling, and spatiotemporal expression analysis—will be crucial to unravel their regulatory networks and interactions within lipid metabolic pathways. This work lays the foundation for refining genetic strategies to optimize the oil content in maize through precision breeding.

4. Materials and Methods

4.1. Plant Material

A total of 274 DH lines were used in this study. These lines were derived from BY4944 and DNF34-2 parents known for their significant differences in oil content traits. Cultivation took place during the 2022 growing season at the National Modern Agriculture Demonstration Park of Heilongjiang Academy of Agricultural Sciences, located in Harbin, China (longitude:126.53°, latitude:45.80°). The experimental field followed a two-row planting configuration, with a plant spacing of 15 cm and a row spacing of 60 cm, adhering to standard agronomic practices. General field management techniques were applied throughout the growing period. For genetic analysis, young leaves from plants in the seedling stage (v3 stage) were placed in liquid nitrogen and stored at −80 °C until further analysis.

4.2. Measurement of Oil Content

At physiological maturity (45 days after pollination), 10 plants each from parental lines and 274 DH lines were randomly selected for ear harvesting, with three biological replicates. The labeled samples were dried in mesh bags and threshed after complete desiccation, with maize kernels subsequently employed for oil content determination.
Quantitative analysis of kernel oil content was performed using the FOSS NIRS system (Infratec™ 1241) through near-infrared spectroscopy scanning (spectral range 950–1650 nm). The spectral acquisition program (Scan mode) was activated to collect 32 spectral scans per sample, with data archived in CAL format.

4.3. Sequencing and Genotyping

Genomic DNA was extracted using the CTAB method [42] to minimize DNA degradation and contamination, as confirmed by 1% agarose gel electrophoresis. Subsequently, the purity and concentrations of the DNA samples were assessed using a NanoDrop UV-VIS spectrophotometer and Qubit 3.0 fluorometer, respectively. High-quality DNA samples were used for the construction of targeted sequencing libraries. Genotyping by target sequencing (GBTS) was employed to generate sequences for analysis, with sequencing performed on the MGI2000 platform using PE150 mode [43]. Raw reads underwent quality control by using Fastp software (version 0.20.0, parameters: -n10-q20-u40), which involved the removal of adapter sequences and paired reads containing more than 10 ‘N’ bases or low-quality (Q ≤ 20) bases exceeding >40% of the read length. The remaining clean reads were aligned to the reference genome sequences (Zm-B73-REFERENCE-NAM-5.0) using Burrows-Wheeler Aligner (BWA-MEM) software (version v0.7.17). Following alignment, statistical analysis was conducted to detect parental marker deletions and heterozygosity; then, aa × bb markers were screened for further analysis. SNPs with a missing rate >10%, a minor allele frequency (MAF) <0.05, and a sample missing rate >10% were filtered out.

4.4. Construction, Evaluation, and QTL Mapping of Genetic Linkage Map

To construct the genetic linkage map, we utilized the mstmap function from the R package ASMap [44], employing a stringent significance threshold (parameter: P.UE = 1 × 10−6). Collinearity analysis between the physical location and genetic distance of each marker was performed to ensure map accuracy.
For the QTL mapping of oil content-related traits, we employed the composite interval mapping (CIM) method using the cim() function of R/qtl using the parameter settings of 1000 permutations and a significance level of >2.5. The QTLs with the highest contribution rates were classified as main effect QTLs. QTLs were named according to a standardized convention, starting with the letter “q” followed by the trait abbreviation, chromosome number, and QTL serial number.

4.5. RNA Sequencing (RNA-Seq) Analysis and RT-qPCR

Thirty high-oil (OH) and thirty low-oil (OL) plants were selected. Ten randomly selected plants per phenotype were pooled to form three biological replicates. At the full maturity stage, kernels were collected from individual maize plants, immediately flash-frozen in liquid nitrogen, and subsequently stored at −80 °C freezer for preservation until further analysis. The modified RNA extraction method for mature maize kernels described by Zhang et al. [45] was employed in this study. RNA quality assessment, library preparation, and sequencing were conducted on the Illumina NovaSeq. 6000 sequencing platform, performed by Guangzhou Genedenovo Biotechnology Co., Ltd, Guangzhou, China. The resulting reads were filtered by HISAT (v.2.0.4) with default parameters and subsequently aligned to the maize reference genome (Zm-B73-REFERENCE-NAM-5.0). Data quality control and the read counts were sequentially performed using FastQC (v.0.11.9) and FeatureCounts (v.2.0.1). The differentially expressed genes (DEGs) between the two samples were identified based on the following criteria: fold-change >1.5 and false discovery rate (FDR) <0.05. The data were analyzed using the TBtools (v2.210).
Following sequencing, functional annotation of homologous proteins and comparative metabolic pathway analyses were performed using databases including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), enabling the identification of differentially expressed genes (DEGs) associated with oil content. Subsequently, the physical coordinates of the QTL mapping intervals were aligned with the maize genome assembly in the NCBI database to delineate genomic features within these regions. Genes exhibiting significant differential expression localized within QTL-corresponding physical intervals were designated as candidate genes.
The expression patterns of candidate genes were validated by RT-qPCR analysis using SYBR Green qPCR Mix (Mona Biotech Co., Ltd., Suzhou, China), with maize ACT (J01238.1) (Gene ID: LOC100282267) serving as the internal reference gene. Detailed information regarding the RT-qPCR amplification system and primer sequences is provided in Supplementary Tables S2 and S3.

5. Conclusions

This study utilized 274 DH lines derived from BY4944 and DNF34-2 to conduct comprehensive genetic analyses, QTL detection, and candidate gene exploration aimed at elucidating the genetic architecture underlying the oil content in silage maize. Leveraging these materials, we constructed a high-density genetic linkage map comprising 5400 SNP markers, enabling the precise localization of genetic loci influencing oil content traits. Through rigorous QTL analysis, we detected a total of 13 QTLs associated with oil content. The most prominent QTL, qOIL-1-1, positioned at 240.93-256.57 Mb on chromosome 1, is potentially a main effect QTL driving oil content variation.
Furthermore, our investigation identified five candidate genes, whose expression trends were validated via RT-qPCR to align with transcriptomic data, thereby reinforcing the robustness of our findings. This study not only provides critical insights into the genetic and molecular mechanisms underlying this economically significant trait but also establishes a rigorous framework for subsequent research endeavors.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/plants14081181/s1, Figure S1: The distribution of markers on the genome; Figure S2: Collinear graph; Figure S3: Correlation coefficient heatmap; Table S1: Sequencing data and filtering of parental and DH line; Table S2: Gene name and primer sequence; Table S3: RT-qPCR amplification system.

Author Contributions

Conceptualization, J.W.; methodology, J.W.; software, Q.W.; validation, Q.Z.; formal analysis, W.H.; investigation, J.W.; resources, J.W.; data curation, Q.W.; writing—original draft preparation, Q.W., J.W.; writing—review and editing, J.W.; visualization, Q.Z.; supervision, D.S.; project administration, Z.S.; funding acquisition, W.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Heilongjiang Province-affiliated Research Institutes’ Scientific Research Business Expense Projects (CZKYF2022-1-B027), National Key R&D Program Project (22YFD1500505-06), Department of Agriculture and Rural Affairs of Heilongjiang Province (2023ZYCX01), Heilongjiang Province Agricultural Science and Technology Innovation Project (CX23TS01, CX23GG01), the Scientific Research Starting Foundation for Postdoctoral of Heilongjiang Province, and Heilongjiang provincial leading talent discipline echelon reserve leader funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

We ensure that the permissions of the Zea mays listed in the manuscript were obtained. All data generated or analyzed during this study are included in this published article and its Supplementary Information Files.

Conflicts of Interest

The authors declare that they have no competing interests.

Abbreviations

The following abbreviations are used in this manuscript:
QTLquantitative trait loci
DHdouble haploid complex interval mapping (CIM)
SNPsingle nucleotide polymorphism
LODlogarithm of odds
TAGtriacylglycerol
PPPpentose phosphate pathway
AICAkaike information criterion
GBTSgenotyping by target sequencing
BWABurrows–Wheeler aligner
GOGene Ontology
KEGGKyoto Encyclopedia of Genes and Genomes
K-SKolmogorov–Smirnov
BPbiological process
CCcell component
MFmolecular function
ABI3ABA-INSENSITIVE 3
KASI-βketoacyl-[acyl carrier protein] synthase I
BRsbrassinosteroids
PVEphenotypic variation explained
ABI3ABA-INSENSITIVE 3
DAGdiacylglycerol
FUS3FUSCA3

References

  1. Hristov, A.N.; Harper, M.T.; Roth, G.; Canale, C.; Huhtanen, P.; Richard, T.L.; DiMarco, K. Effects of ensiling time on corn silage neutral detergent fiber degradability and relationship between laboratory fiber analyses and in vivo digestibility—ScienceDirect. J. Dairy Sci. 2020, 103, 2333–2346. [Google Scholar] [CrossRef] [PubMed]
  2. Wu, J.Z.; Li, S.Y.; Lin, H.; Ma, Y.H.; Pan, L.Y.; Li, D.L.; Sun, D.Q. Genetic Variation and Principal Component Analysis of Silage Maize Quality Traits. Crops 2019, 35, 42–48. [Google Scholar]
  3. Reith, R.R.; Sieck, R.L.; Grijalva, P.C.; Duffy, E.M.; Swanson, R.M.; Fuller, A.M.; Petersen, J.L. 256 Heat stress and β-adrenergic agonists alter the adipose transcriptome and fatty acid mobilization in ruminant livestock. J. Anim. Sci. 2020, 98, 195–196. [Google Scholar] [CrossRef]
  4. Li, Z.Q.; Liu, C.L. Nutrition of Silage and Application Research on Ruminant Production. Acta Ecol. Anim. Domastici 2010, 31, 95–98. [Google Scholar]
  5. Huang, Y.; Wang, H.; Zhu, Y.; Li, S.; Wu, X.; Wu, Y. THP9 enhances seed protein content and nitrogen-use efficiency in maize. Nature 2022, 612, 292–300. [Google Scholar] [CrossRef]
  6. Yang, X.H.; Guo, Y.Q.; Yan, J.B.; Zhang, J.; Song, T.M.; Li, R.S. Major and minor QTL and epistasis contribute to fatty acid compositions and oil concentration in high-oil maize. Theor. Appl. Genet. 2010, 120, 665–678. [Google Scholar] [CrossRef]
  7. Li, L.; Li, H.; Li, J.Y.; Xu, S.T.; Yang, X.H.; Li, J.S.; Yan, J.B. A genome-wide survey of maize lipid-related genes: Candidate genes mining, digital gene expression profiling and co-location with QTL for maize kernel oil. Sci. China Life Sci. 2010, 06, 690–700. [Google Scholar] [CrossRef]
  8. Fang, H.; Fu, X.Y.; Ge, H.Q.; Zhang, A.X.; Wang, B.H. Genetic basis of maize kernel oil-related traits revealed by high-density SNP markers in a recombinant inbred line population. BMC Plant Biol. 2021, 21, 344. [Google Scholar] [CrossRef]
  9. Yang, Z.; Li, X.; Zhang, Y.N.; Jiang, H.W.; Gao, J.; Kuai, B.K.; Ding, Y.L.; Huang, X.Q. Detection of quantitative trait loci for kernel oil and protein concentration in a B73 and Zheng58 maize cross. Genet. Mol. Res. 2016, 15, 10–4238. [Google Scholar] [CrossRef]
  10. Guo, Y.; Yang, X.H.; Chander, S.; Yan, J.B.; Zhang, J.; Song, T.M.; Li, J.S. Identification of unconditional and conditional QTL for oil, protein and starch content in maize. Crop J. 2013, 1, 34–42. [Google Scholar] [CrossRef]
  11. Li, H.; Peng, Z.; Yang, X.; Wang, W.; Fu, J.; Wang, J.; Yan, J. Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels. Nat. Genet. 2013, 45, 43–50. [Google Scholar] [CrossRef] [PubMed]
  12. Liu, D.; Zhang, J.; Liu, X.; Wang, W.; Liu, D.; Teng, Z.; Zhang, Z. Fine mapping and RNA-Seq unravels candidate genes for a major QTL controlling multiple fiber quality traits at the T1 region in upland cotton. BMC Genom. 2016, 17, 295. [Google Scholar] [CrossRef] [PubMed]
  13. Lin, F.; Zhou, L.; He, B.; Zhang, X.; Dai, H.; Qian, Y.; Zhao, H. QTL mapping for maize starch content and candidate gene prediction combined with co-expression network analysis. Theor. Appl. Genet. 2019, 132, 1931–1941. [Google Scholar] [CrossRef] [PubMed]
  14. Wang, T.; Hou, L.; Jian, H.; Di, F.; Li, J.; Liu, L. Combined QTL mapping, physiological and transcriptomic analyses to identify candidate genes involved in Brassica napus seed aging. Mol. Genet. Genom. 2018, 293, 1421–1435. [Google Scholar] [CrossRef]
  15. Zhang, Z.; Dunwell, J.M.; Zhang, Y.M. An integrated omics analysis reveals molecular mechanisms that are associated with differences in seed oil content between Glycine max and Brassica napus. BMC Plant Biol. 2018, 18, 328. [Google Scholar] [CrossRef]
  16. Buckler, E.S.; Holland, J.B.; Bradbury, P.J.; Acharya, C.B.; Brown, P.J.; Browne, C.; McMullen, M.D. The Genetic Architecture of Maize Flowering Time. Science 2009, 325, 714–718. [Google Scholar] [CrossRef]
  17. Qin, Z.H.; Yin, Z.X.; Zhong, X.Q. Genetic analysis of agronomic traits of two doubled haploid populations in tobacco. J. Zhejiang Univ. (Agric. Life Sci.) 2004, 30, 477–481. [Google Scholar]
  18. Zhu, S.; Tang, G.; Yang, Z.; Han, R.; Deng, W.; Shen, X.; Huang, R. Fine mapping of a major QTL, qECQ8, for rice taste quality. BMC Plant Biol. 2024, 24, 1034. [Google Scholar] [CrossRef]
  19. Lv, G.; Jin, X.; Wang, H.; Wang, Y.; Wu, Q.; Wu, H.; Li, S. Cloning a novel reduced-height (Rht) gene TaOSCA1.4 from a QTL in wheat. Front. Plant Sci. 2024, 15, 1381243. [Google Scholar] [CrossRef]
  20. Yang, G.H.; Dong, Y.B.; Li, Y.L.; Wang, Q.L.; Zhou, Q. QTL verification of grain protein content and its correlation with oil content by using connected RIL populations of high-oil maize. Genet. Mol. Res. 2014, 13, 881–894. [Google Scholar] [CrossRef]
  21. Cai, H.; Morishima, H. QTL clusters reflect character associations in wild and cultivated rice. Theor. Appl. Genet. 2002, 104, 1217–1228. [Google Scholar] [CrossRef] [PubMed]
  22. Zerniak, T.; Saenz, J. Lipid membranes modulate the activity of RNA through sequence-dependent interactions. Proc. Natl. Acad. Sci. USA 2022, 119, e2119235119. [Google Scholar] [CrossRef] [PubMed]
  23. Merklinger, L.; Bauer, J.; Pedersen, P.A.; Damgaard, R.B.; Morth, J.P. Phospholipids alter activity and stability of mitochondrial membrane-bound ubiquitin ligase MARCH5. Life Sci. Alliance 2022, 5, e202101309. [Google Scholar] [CrossRef] [PubMed]
  24. Frank, C.; Aebi, M. ALG9 mannosyltransferase is involved in two different steps of lipid-linked oligosaccharide biosynthesis. Glycobiology 2005, 15, 1156–1163. [Google Scholar] [CrossRef]
  25. Watterson, A.; Tatge, L.; Wajahat, N.; Arneaud, S.L.; Solano, F.R.; Beheshti, S.T.; Douglas, P.M. Intracellular lipid surveillance by small G protein geranylgeranylation. Nature 2022, 605, 736–740. [Google Scholar] [CrossRef]
  26. Chen, W.L.; Zhang, Q.Q.; Tang, S.H.; Gong, W.; Hong, Y.Y. Glycerol-3-phosphate acyltransferase in lipid metabolism, growth and response to stresses in plants. Plant Physiol. J. 2018, 54, 725–735. [Google Scholar]
  27. Ma, Y.; Cai, Y.; Ma, X.J.; Cui, G.H.; Tang, J.F.; Zeng, W.; Huang, L.Q. Research progress of P450 in the biosynthesis of bioactive compound of medicinal plants. Acta Pharm. Sin. 2020, 55, 1573–1589. [Google Scholar]
  28. Mueller, J. Vitamin B6 in fat metabolism. Vitam. Horm. 1964, 22, 787–796. [Google Scholar]
  29. Gupta, A.; Rai, S.; Bano, A.; Sharma, S.; Kumar, M.; Binsuwaidan, R.; Pathak, N. ACC deaminase produced by PGPR mitigates the adverse effect of osmotic and salinity stresses in Pisum sativum through modulating the antioxidants activities. Plants 2018, 11, 3419. [Google Scholar] [CrossRef]
  30. Jin, J.; Lu, M.; Li, Q.Y.; Wu, Z.Z.; Zhang, P.; Feng, W.; Jin, Q. Microbial synthesis of sterol-based steroidal compounds: A review. Food Res. Dev. 2018, 39, 205–209. [Google Scholar]
  31. Chu, S.Y.; Sun, B.H.; Tian, X.L.; Wang, X.X. Research progress on the formation and properties of starch-lipid complexes. Food Res. Dev. 2021, 42, 206–211. [Google Scholar]
  32. Chen, M.; Liu, R.; Yi, W. The Discovery and Revelation of Tricarboxylie Acid Cycle. Med. Philos. 2012, 33, 71–73. [Google Scholar]
  33. Li, J.Q.; Shi, X.Z.; Niu, Y.; Ji, B.Z. Acetylcoenzyme A carboxylase: A key metabolic enzyme of fatty acid and progress of its gene clone. Chin. J. Appl. Environ. Biol. 2011, 17, 753–758. [Google Scholar]
  34. Wang, Y.; Zeng, F.; Zhao, Z.; He, L.; He, X.; Pang, H.; Chang, P. Transmembrane protein 68 functions as an MGAT and DGAT enzyme for triacylglycerol biosynthesis. Int. J. Mol. Sci. 2023, 24, 2012. [Google Scholar] [CrossRef]
  35. Liu, Z.; Tian, G. Production of acrylic acid and propionic acid by constructing a portion of the 3-hydroxypropionate/4-hydroxybutyrate cycle from Metallosphaera sedula in Escherichia coli. J. Ind. Microbiol. Biotechnol. 2016, 43, 1659–1670. [Google Scholar] [CrossRef]
  36. Zheng, Y.; Xiang, L.; Kai, W. ABI3 with or without FUS3 highly up-regulates lipid droplet proteins and activates oil accumulation. J. Exp. Bot. 2021, 22, 787–796. [Google Scholar]
  37. Liu, F.; Schnable, P. Functional specialization of maize mitochondrial aldehyde dehydrogenases. Plant Physiol. 2002, 130, 1657–1674. [Google Scholar] [CrossRef]
  38. Li, M.Y.; Ruth, W.; Wang, X.M. Quantitative profiling of Arabidopsis polar glycerolipids in response to phosphorus starvation. Roles of phospholipases D ζ1 and D ζ2 in phosphatidylcholine hydrolysis and digalactosyldiacylglycerol accumulation in phosphorus-starved plants. Plant Physiol. 2006, 142, 750–761. [Google Scholar] [CrossRef]
  39. Yang, Z.; Liu, X.; Wang, K.; Li, Z.; Jia, Q.; Zhao, C.; Zhang, M. ABA-INSENSITIVE 3 with or without FUSCA3 highly up-regulates lipid droplet proteins and activates oil accumulation. J. Exp. Bot. 2022, 73, 2077–2092. [Google Scholar] [CrossRef]
  40. Yang, W.; Wang, G.; Li, J.; Bates, P.D.; Wang, X.; Allen, D.K. Phospholipase Dζ Enhances Diacylglycerol Flux into Triacylglycerol. Plant Physiol. 2017, 174, 110–123. [Google Scholar] [CrossRef]
  41. Mlíčková, K.; Luo, Y.; d’Andrea, S.; Peč, P.; Chardot, T.; Nicaud, J.M. Acyl-CoA oxidase., a key step for lipid accumulation in the yeast Yarrowia lipolytica. J. Mol. Catal. B Enzym. 2014, 28, 81–85. [Google Scholar] [CrossRef]
  42. Chen, D.H.; Ronald, P.C. A Rapid DNA Minipreparation Method Suitable for AFLP and Other PCR Applications. Plant Mol. Biol. Report. 1999, 17, 53–57. [Google Scholar] [CrossRef]
  43. Xu, Y.B.; Yang, Q.; Zheng, H.; Sang, J.; Zhang, Z. Genotyping by target sequencing (GBTS) and its applications. Sci. Agric. Sin. 2020, 53, 2983–3004. [Google Scholar]
  44. Taylor, J.; Butler, D. R Package ASMap: Efficient Genetic Linkage Map Construction and Diagnosis. J. Stat. Softw. 2017, 79, 1–29. [Google Scholar] [CrossRef]
  45. Zhang, Y.Y.; Zhao, J.Q.; Jiang, L.J.; Shi, Y.H.; Chen, L.; Guo, S.L.; Song, J.C.; Jameson, P.E. High quality rna isolation from mature seeds of maize based on modified and optimized protocols. J. Maize Sci. 2015, 23, 591–594. [Google Scholar] [CrossRef]
Plants 14 01181 g001
Figure 1. Histogram of frequency distribution of traits in DH line. B stands for maternal BY4944 and D stands for paternal DNF34-2.
Figure 1. Histogram of frequency distribution of traits in DH line. B stands for maternal BY4944 and D stands for paternal DNF34-2.
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Figure 2. Distribution of core SNPs.
Figure 2. Distribution of core SNPs.
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Figure 3. Genetic linkage map of silage maize. The map shows the position of each marker on the genetic map, the heat map represents the density of the marker on the genetic map, and the color per 1 cM represents the value of 30 cM divided by the number of genetic markers in the 15 cM range of the upper and lower reaches of the 1 cM.
Figure 3. Genetic linkage map of silage maize. The map shows the position of each marker on the genetic map, the heat map represents the density of the marker on the genetic map, and the color per 1 cM represents the value of 30 cM divided by the number of genetic markers in the 15 cM range of the upper and lower reaches of the 1 cM.
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Figure 4. LOD curve of oil content characteristics. The horizontal coordinate is the genetic position of the chromosome (linked group), the vertical coordinate is the LOD value, the blue curve is the LOD value curve, the short black vertical line below the blue line is the location of the marker, and the red dashed line is the threshold line.
Figure 4. LOD curve of oil content characteristics. The horizontal coordinate is the genetic position of the chromosome (linked group), the vertical coordinate is the LOD value, the blue curve is the LOD value curve, the short black vertical line below the blue line is the location of the marker, and the red dashed line is the threshold line.
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Figure 5. (A) volcanic map of differentially expressed genes related to oil content traits. The horizontal axis represents the degree of upregulation or downregulation, the vertical axis represents the number of genes, red represents upregulated genes, and blue represents downregulated genes; (B) GO enrichment analysis results of differentially expressed genes related to oil content; (C) the top 20 pathways for KEGG enrichment analysis of differentially expressed genes related to oil content.
Figure 5. (A) volcanic map of differentially expressed genes related to oil content traits. The horizontal axis represents the degree of upregulation or downregulation, the vertical axis represents the number of genes, red represents upregulated genes, and blue represents downregulated genes; (B) GO enrichment analysis results of differentially expressed genes related to oil content; (C) the top 20 pathways for KEGG enrichment analysis of differentially expressed genes related to oil content.
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Figure 6. Validation of candidate genes by RT-qPCR. The x-axis represents the two parental groups, the high-oil group (OH) and low-oil group (OL), while the y-axis indicates the relative gene expression levels. Lowercase letters above the bars denote the statistical significance of intergroup differences: groups sharing the same letter show no significant difference, whereas groups with different letters exhibit statistically significant differences (p < 0.05).
Figure 6. Validation of candidate genes by RT-qPCR. The x-axis represents the two parental groups, the high-oil group (OH) and low-oil group (OL), while the y-axis indicates the relative gene expression levels. Lowercase letters above the bars denote the statistical significance of intergroup differences: groups sharing the same letter show no significant difference, whereas groups with different letters exhibit statistically significant differences (p < 0.05).
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Table 1. Descriptive statistical results of parental and DH line traits.
Table 1. Descriptive statistical results of parental and DH line traits.
TraitsParentsDH Lines
Maternal ParentMale ParentT ValueMedian ValueK-S ValueSig ValueMeanStandard DeviationEnvironmental VarianceCoefficient of Variation
Oil content/%9.583.41−34.38 **6.490.0340.2005.721.070.1418.25
**: p < 0.05.
Table 2. Basic characteristics of maize genetic map constructed by DH population.
Table 2. Basic characteristics of maize genetic map constructed by DH population.
Linkage GroupMarker NumberGenetic Distance/cMAverage Map Distance/cMMaximum Interval/cM
LG011014627.740.6227.71
LG02609466.700.7733.42
LG03737524.610.7142.76
LG04495344.610.7027.22
LG05573283.570.4919.55
LG06388369.120.9541.30
LG07480353.160.7439.37
LG08474378.410.8040.02
LG09328260.690.7939.37
LG10302255.910.8513.06
ALL54003864.510.7442.76
Table 3. QTL mapping of maize oil content.
Table 3. QTL mapping of maize oil content.
ChromosomeQTLPeak/cMPhysical Interval/MbLOD ValueContribution Rate/%Additive Effect
1qOIL-1-1100.01240.93~256.573.345.06−0.25~−0.22
3qOIL-3-1203.686.53~12.143.032.78−0.36~−0.32
qOIL-3-2243.70160.12~170.984.720.02−0.41~−0.40
qOIL-3-3524.61169.01~178.322.731.06−0.43~−0.41
4qOIL-4-146.4811.40~139.474.710.05−0.43~−0.33
qOIL-4-264.25150.66~166.045.151.89−0.37~−0.34
qOIL-4-376.11157.95~177.073.650.01−0.36~−0.32
5qOIL-5-120.783.82~7.253.373.52−0.31~−0.28
qOIL-5-2283.5815.21~27.662.591.11−0.23~−0.22
6qOIL-6-1155.3488.91~102.893.510.47−0.40~−0.34
qOIL-6-2186.57109.48~130.943.220.48−0.36~−0.26
8qOIL-8-1300.0022.65~116.153.060.10−0.32~−0.28
qOIL-8-2311.2725.34~141.133.000.01−0.33~−0.28
Table 4. Details of candidate genes related to maize oil content.
Table 4. Details of candidate genes related to maize oil content.
Chr.QTLGene IDHomologous Genes in ArabidopsisFunctional Annotation
3qOIL-3-2GRMZM2G133398AT3G24650Regulatory protein viviparous-1 [Zea mays]
3qOIL-3-2GRMZM2G156861AT1G55020lipoxygenase [Zea mays]
4qOIL-4-2GRMZM2G125268AT3G48000mitochondrial aldehyde dehydrogenase RF2B [Zea mays]
6qOIL-6-2GRMZM2G002959AT3G51840acyl-CoA oxidase 4 [Zea mays]
8qOIL-8-1GRMZM2G343588AT3G16785phospholipase D16 [Zea mays]
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Wu, J.; Wang, Q.; Han, W.; Zhao, Q.; Sun, D.; Shen, Z. QTL Mapping and Candidate Gene Screening for Enhancing Oil Content in Silage Maize. Plants 2025, 14, 1181. https://doi.org/10.3390/plants14081181

AMA Style

Wu J, Wang Q, Han W, Zhao Q, Sun D, Shen Z. QTL Mapping and Candidate Gene Screening for Enhancing Oil Content in Silage Maize. Plants. 2025; 14(8):1181. https://doi.org/10.3390/plants14081181

Chicago/Turabian Style

Wu, Jianzhong, Qi Wang, Weibo Han, Qian Zhao, Dequan Sun, and Zhongbao Shen. 2025. "QTL Mapping and Candidate Gene Screening for Enhancing Oil Content in Silage Maize" Plants 14, no. 8: 1181. https://doi.org/10.3390/plants14081181

APA Style

Wu, J., Wang, Q., Han, W., Zhao, Q., Sun, D., & Shen, Z. (2025). QTL Mapping and Candidate Gene Screening for Enhancing Oil Content in Silage Maize. Plants, 14(8), 1181. https://doi.org/10.3390/plants14081181

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