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Article

Genome-Wide Association Study of Abdominal and Intramuscular Fat Deposition Traits in Huainan Yellow-Feathered Chickens

by
Zichun Dai
1,2,3,
Yaxin Li
1,2,3,
Jie Liu
1,2,3,
Rong Chen
1,2,3,
Huanxi Zhu
1,2,3 and
Mingming Lei
1,2,3,*
1
Institute of Animal Science, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
2
Key Laboratory of Crop and Livestock Integration, Ministry of Agriculture, Nanjing 210014, China
3
Jiangsu Province Engineering Research Center of Precision Animal Breeding, Nanjing 210014, China
*
Author to whom correspondence should be addressed.
Animals 2025, 15(22), 3342; https://doi.org/10.3390/ani15223342
Submission received: 30 October 2025 / Revised: 15 November 2025 / Accepted: 17 November 2025 / Published: 19 November 2025
(This article belongs to the Special Issue Advances in Genetic Analysis of Important Traits in Poultry)
Browse Figure
Versions Notes

Simple Summary

The Huainan yellow-feathered chicken is a prized local breed known for its high-quality meat. However, excessive abdominal fat deposition adversely affects feed efficiency and carcass quality. Since this fat is difficult to measure in live birds, breeding for leaner chickens is challenging. In this study, we used a genetic approach to identify DNA markers linked to fat deposition. We discovered several key genes that control processes like appetite, fat breakdown, and cholesterol transport. Our findings provide new tools for breeders to select chickens that are genetically inclined to be leaner, which will help improve meat quality and reduce waste fat in this prized local breed.

Abstract

The Huainan yellow-feathered chicken is a prized local breed known for its high-quality meat. However, excessive abdominal fat deposition adversely affects feed efficiency and carcass quality. This study aimed to identify genetic markers and candidate genes associated with fat traits to facilitate marker-assisted selection (MAS) using genome-wide association studies (GWAS). A total of 220 chickens were phenotyped for abdominal fat weight (AFW), abdominal fat percentage (AFP), intramuscular fat of pectoral muscle (IFPM), and intramuscular fat of leg muscle (IFLM). GWAS based on whole-genome resequencing revealed significant SNPs for AFW and AFP on chromosomes 1, 2, 7, 10, 13, and 35, annotating genes including GRIA1, CYP1A1, CYP1A2, and SCAMP2. For IFPM and IFLM, significant loci were identified on chromosomes 1, 2, 4, 5, 6, 9, 12, 23, 25, 26, and 28, highlighting genes such as LRP4, FABP3, and ADAMTS9. Functional enrichment analysis showed involvement of steroid hormone biosynthesis, retinol metabolism, and cytochrome P450 pathways in abdominal fat deposition, while Wnt and MAPK signaling pathways regulated intramuscular fat. These findings provide molecular targets for genetic selection to improve fat traits in Huainan chickens.

1. Introduction

Chinese yellow-feathered broilers are widely recognized for their high-quality meat and represent an important source of premium animal protein. Although long-term systematic breeding has substantially enhanced their meat production performance, excessive abdominal fat deposition has become an increasingly prominent issue [1]. This not only reduces feed efficiency and increases rearing costs, but may also compromise carcass yield, meat quality attributes, and consumer health [2]. Currently, abdominal fat traits cannot be measured directly in live animals, and accurate assessment relies solely on post-slaughter dissection—a process that is costly, time-consuming, and labor-intensive. These limitations severely impede the genetic improvement of abdominal fat traits through phenotypic selection [3].
Fat deposition is influenced by both genetic and nutritional factors, including an individual’s genetic background, genomic polymorphisms, and gene expression patterns [4]. Carcass traits generally exhibit moderate to high heritability, though this varies across populations and specific traits [5]. Previous studies have indicated that abdominal fat traits in chickens are highly heritable, with an estimated heritability of approximately 0.82, highlighting substantial potential for genetic improvement [6]. Therefore, identifying molecular markers associated with abdominal fat deposition, elucidating the underlying genetic mechanisms, and implementing marker-assisted selection (MAS) are of great importance for reducing abdominal fat content and enhancing breeding efficiency in Huainan yellow chickens.
Genome-wide association studies (GWAS) offer a high-throughput strategy for systematically identifying single nucleotide polymorphisms (SNPs) significantly associated with target traits across the genome. Since the pioneering GWAS on age-related macular degeneration by Klein et al. in 2005 [7], this approach has been extensively applied in genetic analyses and molecular breeding for economically important traits. In poultry, multiple SNP loci and candidate genes related to fat deposition—particularly abdominal fat—have been identified in both white-feathered broilers and Chinese indigenous yellow chickens [8]. SNPs can influence phenotypic variation by interacting with transcription factors and regulating the expression of key genes involved in adipocyte differentiation and proliferation [9]. These findings underscore the importance of clarifying the genetic basis of fat deposition and discovering potential candidate genes.
In this study, we performed a GWAS using whole-genome resequencing to investigate fat-related traits in Huainan yellow chickens. Our objective was to identify significant SNPs and key genes associated with abdominal fat deposition, with the aim of providing novel genetic markers for marker-assisted selection and laying a theoretical foundation for genetic improvement of fat traits in this breed.

2. Materials and Methods

2.1. Ethics Approval

The experimental protocol was approved by the Research Committee of the Jiangsu Academy of Agricultural Sciences and was conducted in compliance with the Regulations for the Administration of Affairs Concerning Experimental Animals (Decree No. 63), issued by the Jiangsu Academy of Agricultural Sciences on 8 July 2014.

2.2. Population and Experimental Design

The experiment was conducted at the Liuhe Experimental Poultry Farm of the Jiangsu Academy of Agricultural Sciences in Nanjing, China. A total of 220 healthy 35-day-old Huainan yellow chickens (a local breed from Anhui Province), comprising 100 roosters and 120 hens—were used in this study. These birds (100 roosters and 120 hens) were randomly selected from a larger population to ensure a representative sample for the study. All birds were raised under environmentally controlled conditions (temperature maintained at 24 °C; relative humidity 85–90%) using an online flat-feeding system. They were provided with ad libitum access to water and fed a standard growth pellet diet containing 20% crude protein and 12.54 MJ/kg metabolizable energy. The detailed composition and nutritional content of the diet provided to the chickens are summarized in Supplementary Table S1. Uniform immunization was performed following the routine broiler vaccination protocol.
Slaughter and sampling were carried out when the chickens reached 110 days of age. After a 24 h fasting period, blood samples were collected from the inferior vena cava using EDTA-coated tubes for anticoagulation. Birds were stunned using an electric shock (120 V, 50 Hz, 5 s) prior to exsanguination, which was performed by severing the jugular vein and carotid artery on one side of the neck. After slaughter, feathers, head, internal organs, and claws were removed, and the remaining carcass, including abdominal fat, was weighed to obtain the carcass weight. Abdominal adipose tissue and other extragastric fat deposits were carefully separated and weighed. The heart and liver were also excised and weighed accordingly.

2.3. Statistical Analysis

2.3.1. Phenotypic Data Statistical Analysis

Carcass weight (CW), liver weight (LVW), abdominal fat (AFW), and heart weight (HEW) were calculated using Microsoft Excel. The indices of liver (LVR), abdominal fat (AFP), and heart (HER) were calculated as follows:
Liver or abdominal fat or heart index = [tissue weight g/carcass weight g] × 100%.
Descriptive statistical analysis was conducted on the abdominal fat weight and abdominal fat percentage of Huainan yellow chickens using SPSS software (SPSS 29.0, SPSS Inc., Chicago, IL, USA). The sample size (N), maximum value (Max), minimum value (Min), mean value (Mean), and standard deviation (SD) were calculated.
Analysis of correlation was performed using SPSS Statistics (SPSS 29.0, SPSS Inc., Chicago, IL, USA). Normality of the data distribution for all continuous variables was assessed using the Shapiro–Wilk test and by visual inspection of Q-Q plots. The association between continuous variables was evaluated using bivariate correlation analysis. Correlation coefficients (r) were interpreted as follows: |r| < 0.3 indicated a weak correlation, 0.3 ≤ |r| < 0.5 indicated a moderate correlation, and |r| ≥ 0.5 indicated a strong correlation.

2.3.2. DNA Extraction and Low Depth Sequencing

Genomic DNA was extracted from blood samples with a high-throughput DNA extraction kit, and its purity and integrity were detected by 1.5% agarose gel electrophoresis. For qualified DNA samples, Tn5 transposase was used for random disruption, followed by PCR amplification and magnetic bead sorting to construct a sequencing library. The insertion fragment size of the library was controlled between 300–600 bp. Subsequently, low-depth whole genome resequencing was performed on the DNBSEQ-T7 platform, with an average sequencing depth of 10× across all samples. Perform quality control and sequence alignment on the raw sequencing data to obtain high-quality and effective data, resulting in a final dataset with a mean mapping rate of >95% to the reference genome (GRCg7b).

2.3.3. Genotyping Data Processing and Population Structure Analysis

Low-quality sequencing data were filtered using Trimomatic software (v0.39), and high-quality sequences were aligned to the reference genome of domestic chickens (GRCg7b) using BWA software (version 0.7.17), which provides a high-quality and widely used genomic framework for variant discovery in domestic chickens. SNP detection was performed using GATK V4.1.8.1 software, and strict quality control was carried out according to the following criteria: loci with Quality by Depth (QD) < 2.0, Mapping Quality (MQ) < 40.0, or Fisher Strand (FS) > 60.0 were excluded. After filtering, a total of 514,882 high-quality SNPs were retained for subsequent analysis. To eliminate the interference of linkage disequilibrium on analysis, independent SNPs were screened using PLINK software (v1.90), and principal component analysis (PCA) was performed based on this to evaluate population structure.

2.3.4. GWAS of Four Fat Traits

Based on a high-quality SNP dataset, a genome-wide association study was performed for each fat trait using the Fixed and Random Model Circulating Probability Unification (FarmCPU) model, as implemented in the R package GAPIT3 Version 3. The model is as follows:
Y = Xβ + Sα + Zu + e
where Y is the phenotype values, X and Z are the design matrices, S is the SNP genotype matrix, β is the fixed effect vector for environments and sex, α is effect vector for the SNP marker genotype, u is the random effect caused by polygenic background, and e is the residual effect. The Bonferroni correction method was used to stringently adjust for multiple tests to control false positive rates. The significance threshold at the genomic level was set to p < 0.01/N, and the suggestive association threshold was set to p < 0.05/N, where N is the number of independent SNPs used for analysis.

2.3.5. Candidate Genes Annotation and Functional Enrichment Analysis

A 1% threshold was applied in accordance with previous literature and the expected average proportion of genetic variance explained by a single chromosome segment. For each window, the most significant SNP was selected as the midpoint, and the region was extended by 0.1 Mb in both upstream and downstream directions. These expanded regions were subsequently used for candidate gene screening. Candidate genes within the identified QTL regions were retrieved from the Ensembl database (Gallus_gallus-7.0; https://asia.ensembl.org, accessed on 10 July 2025). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted based on the candidate genes using Metascape v2.2.2.20723 [10] for functional annotation, visualization, and integrated discovery.

3. Results and Discussion

3.1. Statistical Data of Fat Traits and Fat-Related Traits

Descriptive statistics for nine fat and fat-related traits in Huainan yellow-feathered chickens are summarized in Table 1. The mean values of the traits were as follows: CW (1615.27), LVW (35.59), AFW (53.79), HEW (8.99), LVR (2.26), AFP (3.18), HER (0.56), intramuscular fat of pectoral muscles (IFPM) (4.19), and intramuscular fat of leg muscles IFLM (7.48). The coefficients of phenotypic variation (CV) ranged from 19.15% (HER) to 51.82% (AFW), with AFW and AFP showing the highest coefficients of variation. This suggests that heart development is relatively stable, whereas abdominal fat deposition exhibits substantial variability, reflecting considerable genetic diversity across the measured traits. The CV for both AFW and AFP in Huainan yellow chickens was high, which is consistent with Pan’s findings in dwarf yellow-plumage chickens [8]. The result indicated that genomic-assisted selection is a promising strategy to improve fat traits in breeding programs.
To further explore the relationships among these traits, we performed a correlation analysis (Table 2). AFP showed significant positive correlations with CW (r = 0.264, p < 0.01) and IFLM (0.275, p < 0.01), and significant negative correlations with LVW (r = −0.167, p < 0.05), LVR (r = −0.329, p < 0.01), and HER (r = −0.345, p < 0.01). IFPM was positively correlated with LVW (r = 0.169, p < 0.05), and LVR (r = 0.242, p < 0.01). IFLM demonstrated relatively strong phenotypic associations with AFW (r = 0.276, p < 0.01) and AFP (r = 0.275, p < 0.01), whereas correlations between abdominal fat traits and pectoral muscle fat traits were weak. In addition, CW was significantly correlated with HEW (r = 0.701, p < 0.01), LVW was significantly correlated with LVR (r = 0.743, p < 0.01), HEW was significantly correlated with HER (r = 0.875, p < 0.01), AFW was significantly correlated with AFP, with a correlation coefficient of up to 0.989 (p < 0.01).

3.2. GWAS for AFW and AFP

As important quantitative traits in poultry, abdominal fat deposition plays a critical role in assessing chicken production performance. To pinpoint potential candidate genes, we first identified key genomic regions. Specifically, these regions were defined by extending 100 kb upstream and downstream from each GWAS significant SNP and then intersecting the intervals with protein-coding gene annotations.
For AFW, six significant SNPs (p < 1 × 10−9) were detected on Gallus gallus chromosomes (GGC) 1, 2, 10, 13, and 35, with genetic effects ranging from −12.54 to 30.32. A total of 28 genes were annotated within these associated regions (Figure 1; Table 3). For abdominal fat percentage (AFP), five significant regions were identified on GGC 1, 7, 9, 10, and 13, with effect sizes between −1.75 and 1.93, encompassing 16 candidate genes.
Notably, due to the strong phenotypic correlation between AFW and AFP, a shared significant SNP (rs11945109, T/A) was identified on GGC13. This variant was found within an intron of the glutamate ionotropic receptor AMPA type subunit 1 (GRIA1) gene, a gene spanning the 11.92–12.04 Mb region on GGC13, at position 11,945,109. The GRIA1-encoded GluA1 protein is a core component of the AMPA receptor and has been implicated in central regulation of appetite and energy balance [11,12], suggesting its potential role in modulating feed intake and fat deposition. Supporting this, studies in mice have shown that high-fat diet feeding significantly upregulates hypothalamic expression of AMPA receptor subunits, including GRIA1 [13]. These findings collectively nominate GRIA1 as a promising candidate gene influencing both AFW and AFP.
Another SNP, rs66761782 on GGC13, was localized within a region containing 16 genes, several of which have established roles in lipid metabolism. Among these, cytochrome P450 family members CYP1A1, CYP1A2, and CYP11A1 are involved in steroidogenesis and xenobiotic metabolism. CYP1A1 promotes preadipocyte maturation [14,15], CYP11A1 initiates steroid hormone synthesis by converting cholesterol to pregnenolone [16], and CYP1A2 polymorphisms modulate metabolic responses to dietary factors and obesity susceptibility [17,18]. Other relevant genes in this region include secretory carrier membrane protein 2 (SCAMP2), which participates in cellular cholesterol efflux in foam cells [19,20]; nudix motif 3 (NUDT3) gene negatively regulates adipogenesis and lipid storage [21,22]; and SH3-domain binding protein 4 (SH3BP4) inhibits adipocyte differentiation via mTORC1 suppression [23].
We also identified UDP-glucuronosyltransferase 1 A1 (UGT1A1), which encodes the key enzyme in bilirubin conjugation. Bilirubin, a heme catabolite, has been inversely associated with obesity in both clinical and animal studies [24]. It functions as an activator of peroxisome proliferator activated receptor α (PPARα), promoting fatty acid oxidation and reducing hepatic lipid accumulation [25]. These mechanisms support the notion that bilirubin metabolism may influence obesity and related metabolic disorders.

3.3. GWAS for IFPM and IFLM

For IFPM, genome-wide association analysis identified eight significant SNPs distributed across Gallus gallus chromosomes (GGC) 1, 6, 9, 12, 23, 26, and 28. These SNPs showed genetic effects ranging from 0.24 to 1.37, and a total of 44 genes were annotated within the corresponding genomic intervals (Figure 1; Table 3). For IFLM, seven significant regions were detected on GGC 1, 2, 4, 5, and 25, with effect sizes between –1.31 and 2.76, encompassing 41 candidate genes.
Several promising candidate genes located within these regions have established roles in lipid metabolism and adipogenesis. Low-density lipoprotein receptor-related protein 4 (LRP4), which encodes a membrane-bound receptor, modulates the uptake and storage of triglyceride-rich lipoproteins in adipose tissue [26,27]. Adipose-specific knockout of LRP4 in mice reduces fat mass, decreases body weight, and improves systemic glucose and lipid homeostasis [28]. RAD51B, a member of the RAD51 protein family, has been associated with body mass index and obesity risk in human genetic research [29]. Pygo2 functions as a key component of the Wnt/β-catenin signaling pathway and inhibits adipogenesis by suppressing master regulators such as C/EBPα and PPARγ, thereby influencing susceptibility to obesity and diabetes [30].
Fatty acid-binding protein 3 (FABP3), highly expressed in muscle tissues, facilitates the intracellular transport of long-chain fatty acids to mitochondria for β-oxidation or to the endoplasmic reticulum for esterification and storage [31,32]. This process supports energy production in muscle and limits ectopic lipid accumulation [33,34]. Metalloproteinase with thrombospondin 9A (DAMTS9), which encodes an extracellular protease involved in ECM remodeling, modulates the tissue microenvironment and has been associated in genome-wide studies with systemic obesity and fat distribution [35,36,37]. Additionally, four and a half LIM domains protein 2 (FHL2), which encodes a transcriptional cofactor, expressed in muscle, appears to suppress adipogenic differentiation of mesenchymal stem cells [38,39]. Consistent with this, FHL2-deficient mice are resistant to high-fat diet-induced weight gain and maintain glucose homeostasis [40].

3.4. KEGG Analysis

KEGG enrichment analysis revealed several significantly enriched pathways associated with the identified candidate genes (Table 4). For the AFW trait, nine signaling pathways were significantly enriched. Among them, the steroid hormone biosynthesis and ovarian steroidogenesis pathways suggest an important role of steroid hormone biosynthesis in abdominal fat development in broilers [41]. The enrichment of multiple additional pathways was observed. Among them, caffeine, linoleic acid, retinol, tryptophan, and xenobiotic/drug metabolism by cytochrome P450, along with chemical carcinogenesis (DNA adducts), all functionally converged on CYP1A1 and CYP1A2. This pattern underscores the central role of the cytochrome P450 system in these metabolic networks. Notably, caffeine metabolism is predominantly mediated by CYP1A2 [42]. Moreover, bioactive metabolites derived from these pathways—such as retinoic acid, epoxyeicosatrienoic acids (EETs), and tryptophan derivatives—often serve as ligands for nuclear or membrane receptors. This interaction creates a regulatory feedback that influences both cytochrome P450 expression and overall lipid deposition [43,44]. AFW and AFP shared one common pathway—retinol metabolism—whose active metabolite, retinoic acid, is a known suppressor of preadipocyte differentiation [45].
For the IFPM trait, six pathways were enriched, including the MAPK signaling pathway, Wnt signaling pathway, phospholipase D signaling pathway, endocytosis, Fc gamma R-mediated phagocytosis, and choline metabolism in cancer. In contrast, for IFLM, three pathways were significantly enriched: other glycan degradation, sphingolipid metabolism, and terpenoid backbone biosynthesis.
Several of these pathways have established roles in muscle and fat biology. The Wnt/β-catenin pathway promotes myoblast differentiation while inhibiting adipogenesis [46,47]. The phospholipase D pathway participates in diverse cellular processes, including signal transduction, lipid droplet formation, and apoptosis. Phospholipid metabolism also influences meat quality attributes such as texture, flavor, and nutritional value [48,49]. Sphingolipids, including ceramides, can induce insulin resistance and impair glucose uptake in muscle cells [50], or modulate fatty acid β-oxidation [51], thereby affecting energy balance and lipid storage. The enrichment of the glucosylceramidase gene (LOC107050229) within the sphingolipid metabolism pathway further suggests its potential role in regulating intramuscular fat deposition in chickens. Although abdominal fat and intramuscular fat were driven by different key genes and enriched in different upstream metabolic pathways, they shared core cellular signaling pathways such as Wnt and MAPK. These pathways may serve as integration points, synergistically regulating the overall deposition and distribution of body fat through differences in activity in different tissues.
This study has limitations including a modest sample size and single-breed design, which may restrict power to detect minor variants and generalizability. Candidate gene functions remain computationally inferred without experimental validation. Future work should integrate transcriptomic, epigenomic, or metabolomics data to verify causative mechanisms and broaden applicability across breeds. Once validated, these SNPs can serve as molecular markers for the precision breeding of Huainan yellow chickens, enabling the early selection of ideal fat traits. This process is not only expected to improve feed efficiency by reducing abdominal fat deposition, but may also increase intramuscular fat (IMF) content, resulting in excellent meat quality and significantly accelerating genetic processes.

4. Conclusions

This study represents the comprehensive GWAS of fat deposition traits in Huainan yellow chickens utilizing whole-genome resequencing. We have successfully identified multiple significant SNPs and candidate genes associated with fat traits in Huainan yellow chickens (e.g., GRIA1, CYP1A1/2, LRP4, FABP3), as well as key biological pathways (such as steroid hormone biosynthesis and Wnt signaling). These findings provide valuable molecular markers and theoretical support for the genetic improvement of fat-related traits. By utilizing this genetic information, individuals with low abdominal fat and high intramuscular fat content can be selected to improve feed efficiency, carcass quality, shorten generation intervals, and enhance breeding efficiency, providing valuable resources for future research on the genetic basis of fat traits.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ani15223342/s1, Table S1. The main nutritional components of feed.

Author Contributions

Conceptualization, Z.D. and R.C.; methodology, Y.L.; resources, Y.L. and Z.D.; writing—original draft preparation, M.L. and H.Z.; Software, R.C.; writing—review and editing, J.L.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Biological Breeding National Science and Technology Major (project grant number: 2023ZD04064).

Institutional Review Board Statement

The experimental procedures were approved by the Research Committee of the Jiangsu Academy of Agricultural Sciences and were conducted in accordance with the Regulations for the Administration of Affairs Concerning Experimental Animals (Decree No. 63) of the Jiangsu Academy of Agricultural Science on 8 July 2014.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Tian, W.; Nie, R.; Zhang, W.; Ling, Y.; Tian, H.; Zhang, B.; Zhang, H.; Wu, C. Research progress on genetic characteristics and genes regulation of abdominal fat in broiler chickens (Gallus gallus). J. Agric. Biotechnol. 2023, 31, 2387–2400. [Google Scholar] [CrossRef]
  2. Leng, L.; Zhang, H.; Wang, W.; Wang, S.; Li, H. Research progress of lean broiler selection methods. China Poult. 2019, 41, 102298. [Google Scholar] [CrossRef]
  3. Zhang, Z.; Li, D.; Wang, Y.; Li, G.; Liu, L.; Xu, H. Genome-wide association study identifies genetic markers associated with abdominal fat percentage in chickens. Poult. Sci. 2022, 101, 101789. [Google Scholar] [CrossRef]
  4. Wang, H.; Li, H.; Wang, Q.; Zhang, X.; Wang, Y.; Liu, Z. Integrated analysis of transcriptome and GWAS reveals candidate genes for abdominal fat deposition in chickens. Poult. Sci. 2022, 101, 101651. [Google Scholar] [CrossRef]
  5. Zerehdaran, S.; Vereijken, A.L.; van Arendonk, J.A.; van der Waaijt, E.H. Estimation of genetic parameters for fat deposition and carcass traits in broilers. Poult. Sci. 2004, 83, 521–525. [Google Scholar] [CrossRef] [PubMed]
  6. Cahaner, A.; Nitsan, Z. Evaluation of simultaneous selection for live body weight and against abdominal fat in broilers. Poult. Sci. 1985, 64, 1257–1263. [Google Scholar] [CrossRef] [PubMed]
  7. Klein, R.J.; Zeiss, C.; Chew, E.Y.; Tsai, J.Y.; Sackler, R.S.; Haynes, C.; Henning, A.K.; SanGiovanni, J.P.; Mane, S.M.; Mayne, S.T.; et al. Complement factor H polymorphism in age-related macular degeneration. Science 2005, 308, 385–389. [Google Scholar] [CrossRef] [PubMed]
  8. Pan, R.; Qi, L.; Xu, Z.; Zhang, D.; Nie, Q.; Zhang, X.; Luo, W. Weighted single-step GWAS identified candidate genes associated with carcass traits in a Chinese yellow-feathered chicken population. Poult. Sci. 2024, 103, 103341. [Google Scholar] [CrossRef]
  9. Shen, L.; Bai, X.; Zhao, L.; Zhou, J.; Chang, C.; Li, X.; Cao, Z.; Li, Y.; Luan, P.; Li, H.; et al. Integrative 3D genomics with multi-omics analysis and functional validation of genetic regulatory mechanisms of abdominal fat deposition in chickens. Nat. Commun. 2024, 15, 19274. [Google Scholar] [CrossRef]
  10. Zhou, Y.; Zhou, B.; Pache, L.; Chang, M.; Khodabakhshi, A.H.; Tanaseichuk, O.; Benner, C.; Chanda, S.K. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 2019, 10, 1523. [Google Scholar] [CrossRef]
  11. Stanley, B.G.; Willett, V.L., 3rd; Donias, H.W.; Ha, L.H.; Spears, L.C. The lateral hypothalamus: A primary site mediating excitatory amino acid-elicited eating. Brain Res. 1993, 630, 41–49. [Google Scholar] [CrossRef] [PubMed]
  12. Stanley, B.G.; Willett, V.L., 3rd; Donias, H.W.; Dee, M.G., 2nd; Duva, M.A. Lateral hypothalamic NMDA receptors and glutamate as physiological mediators of eating and weight control. Am. J. Physiol. 1996, 270, R443–R449. [Google Scholar] [CrossRef] [PubMed]
  13. Adermark, L.; Gutierrez, S.; Lagström, O.; Hammarlund, M.; Licheri, V.; Johansson, M.E. Weight gain and neuroadaptations elicited by high fat diet depend on fatty acid composition. Psychoneuroendocrinology 2021, 126, 105143. [Google Scholar] [CrossRef] [PubMed]
  14. Montie, E.W.; Fair, P.A.; Bossart, G.D.; Mitchum, G.B.; Houde, M.; Muir, D.C.; Letcher, R.J.; McFee, W.E.; Starczak, V.R.; Stegeman, J.J.; et al. Cytochrome P4501A1 expression, polychlorinated biphenyls and hydroxylated metabolites, and adipocyte size of bottlenose dolphins from the Southeast United States. Aquat. Toxicol. 2008, 86, 397–412. [Google Scholar] [CrossRef]
  15. Ellero, S.; Chakhtoura, G.; Barreau, C.; Langouët, S.; Benelli, C.; Penicaud, L.; Beaune, P.; de Waziers, I. Xenobiotic-metabolizing cytochromes p450 in human white adipose tissue: Expression and induction. Drug Metab. Dispos. 2010, 38, 679–686. [Google Scholar] [CrossRef]
  16. Shih, M.C.; Chiu, Y.N.; Hu, M.C.; Guo, I.C.; Chung, B.C. Regulation of steroid production: Analysis of Cyp11a1 promoter. Mol. Cell. Endocrinol. 2011, 336, 80–84. [Google Scholar] [CrossRef]
  17. Gkouskou, K.G.; Georgiopoulos, G.; Vlastos, I.; Lazou, E.; Chaniotis, D.; Papaioannou, T.G.; Mantzoros, C.S.; Sanoudou, D.; Eliopoulos, A.G. CYP1A2 polymorphisms modify the association of habitual coffee consumption with appetite, macronutrient intake, and body mass index: Results from an observational cohort and a cross-over randomized study. Int. J. Obes. 2022, 46, 162–168. [Google Scholar] [CrossRef]
  18. Ching, M.E.A.; Hoyeck, M.P.; Basu, L.; Merhi, R.; Poleo-Giordani, E.; van Zyl, E.; Crawley, A.M.; Bruin, J.E. CYP1A1/1A2 enzymes mediate glucose homeostasis and insulin secretion in mice in a sex-specific manner. Am. J. Physiol. Endocrinol. Metab. 2025, 328, E885–E898. [Google Scholar] [CrossRef]
  19. Yue, C.; Xie, S.; Zhong, J.; Zhao, H.; Lin, Z.; Zhang, L.; Xu, B.; Luo, Y. SCAMP2/5 as diagnostic and prognostic markers for acute myeloid leukemia. Sci. Rep. 2021, 11, 17012. [Google Scholar] [CrossRef]
  20. Lin, Y.; Pan, H.; Ding, H.; Ma, W.; Zhang, Z. Role of SCAMP2 and Rab8a in cholesterol transport of primary mouse macrophage-derived foam cells. J. Guangdong Med. Univ. 2022, 40, 132–136. [Google Scholar]
  21. An, Y.; Han, P.; Zhang, C.; Yue, Y.; Wen, C.; Meng, Y.; Li, H.; Li, X. The role of NUDT3 in lipid accumulation and its functional variants related to backfat thickness in pigs. Int. J. Biol. Macromol. 2025, 307, 141901. [Google Scholar] [CrossRef]
  22. Zeng, H.; Zhong, Z.; Xu, Z.; Teng, J.; Wei, C.; Chen, Z.; Zhang, W.; Ding, X.; Li, J.; Zhang, Z. Meta-analysis of genome-wide association studies uncovers shared candidate genes across breeds for pig fatness trait. BMC Genom. 2022, 23, 786. [Google Scholar] [CrossRef]
  23. Kim, Y.M.; Stone, M.; Hwang, T.H.; Kim, Y.G.; Dunlevy, J.R.; Griffin, T.J.; Kim, D.H. SH3BP4 is a negative regulator of amino acid-Rag GTPase-mTORC1 signaling. Mol. Cell 2012, 46, 833–846. [Google Scholar] [CrossRef]
  24. Kipp, Z.A.; Xu, M.; Bates, E.A.; Lee, W.H.; Kern, P.A.; Hinds, T.D., Jr. Bilirubin Levels Are Negatively Correlated with Adiposity in Obese Men and Women, and Its Catabolized Product, Urobilin, Is Positively Associated with Insulin Resistance. Antioxidants 2023, 12, 170. [Google Scholar] [CrossRef]
  25. Hinds, T.D., Jr.; Creeden, J.F.; Gordon, D.M.; Stec, D.F.; Donald, M.C.; Stec, D.E. Bilirubin Nanoparticles Reduce Diet-Induced Hepatic Steatosis, Improve Fat Utilization, and Increase Plasma beta-Hydroxybutyrate. Front. Pharmacol. 2020, 11, 594574. [Google Scholar] [CrossRef] [PubMed]
  26. May, P.; Woldt, E.; Matz, R.L.; Boucher, P. The LDL receptor-related protein (LRP) family: An old family of proteins with new physiological functions. Ann. Med. 2007, 39, 219–228. [Google Scholar] [CrossRef] [PubMed]
  27. Calvier, L.; Herz, J.; Hansmann, G. Interplay of Low-Density Lipoprotein Receptors, LRPs, and Lipoproteins in Pulmonary Hypertension. JACC Basic Transl. Sci. 2022, 7, 164–180. [Google Scholar] [CrossRef] [PubMed]
  28. Kim, S.P.; Da, H.; Li, Z.; Kushwaha, P.; Beil, C.; Mei, L.; Xiong, W.C.; Wolfgang, M.J.; Clemens, T.L.; Riddle, R.C. Lrp4 expression by adipocytes and osteoblasts differentially impacts sclerostin’s endocrine effects on body composition and glucose metabolism. J. Biol. Chem. 2019, 294, 6899–6911. [Google Scholar] [CrossRef]
  29. Barbitoff, Y.A.; Serebryakova, E.A.; Nasykhova, Y.A.; Predeus, A.V.; Polev, D.E.; Shuvalova, A.R.; Vasiliev, E.V.; Urazov, S.P.; Sarana, A.M.; Scherbak, S.G.; et al. Identification of Novel candidate markers of type 2 diabetes and obesity in Russia by exome sequencing with a limited sample size. Genes 2018, 9, 415. [Google Scholar] [CrossRef]
  30. Xie, Y.Y.; Mo, C.L.; Cai, Y.H.; Wang, W.J.; Hong, X.X.; Zhang, K.K.; Liu, Q.F.; Liu, Y.J.; Hong, J.J.; He, T.; et al. Pygo2 Regulates Adiposity and Glucose Homeostasis via β-Catenin-Axin2-GSK3β Signaling Pathway. Diabetes 2018, 67, 2569–2584. [Google Scholar] [CrossRef]
  31. Cui, H.; Zheng, M.; Zhao, G.; Liu, R.; Wen, J. Identification of differentially expressed genes and pathways for intramuscular fat metabolism between breast and thigh tissues of chickens. BMC Genom. 2018, 19, 55. [Google Scholar] [CrossRef]
  32. Kochansky, C.J.; Lyman, M.J.; Fauty, S.E.; Vlasakova, K.; D’mello, A.P. Administration of Fenofibrate Markedly Elevates Fabp3 in Rat Liver and Plasma and Confounds Its Use as a Preclinical Biomarker of Cardiac and Muscle Toxicity. Lipids 2018, 53, 947–960. [Google Scholar] [CrossRef] [PubMed]
  33. Fu, Y.; Hao, X.; Nie, J.; Zhang, H.; Shang, P.; Zhang, B.; Zhang, H. MUSTN1 and FABP3 interact to regulate adipogenesis and lipid deposition. J. Lipid Res. 2025, 66, 100804. [Google Scholar] [CrossRef] [PubMed]
  34. Liu, Y.; Liang, S.; Wang, K.; Zi, X.; Zhang, R.; Wang, G.; Kang, J.; Li, Z.; Dou, T.; Ge, C. Physicochemical, nutritional properties and metabolomics analysis fat deposition mechanism of Chahua chicken No. 2 and Yao chicken. Genes 2022, 13, 1358. [Google Scholar] [CrossRef] [PubMed]
  35. Mead, T.J.; Du, Y.; Nelson, C.M.; Gueye, N.A.; Drazba, J.; Dancevic, C.M.; Vankemmelbeke, M.; Buttle, D.J.; Apte, S.S. ADAMTS9-Regulated Pericellular Matrix Dynamics Governs Focal Adhesion-Dependent Smooth Muscle Differentiation. Cell Rep. 2018, 23, 485–498. [Google Scholar] [CrossRef]
  36. Graae, A.S.; Grarup, N.; Ribel-Madsen, R.; Lystbæk, S.H.; Boesgaard, T.; Staiger, H.; Fritsche, A.; Wellner, N.; Sulek, K.; Kjolby, M.; et al. ADAMTS9 Regulates Skeletal Muscle Insulin Sensitivity Through Extracellular Matrix Alterations. Diabetes 2019, 68, 502–514. [Google Scholar] [CrossRef]
  37. Liu, C.T.; Monda, K.L.; Taylor, K.C.; Lange, L.; Demerath, E.W.; Palmas, W.; Wojczynski, M.K.; Ellis, J.C.; Vitolins, M.Z.; Liu, S.; et al. Genome-wide association of body fat distribution in African ancestry populations suggests new loci. PLoS Genet. 2013, 9, e1003681. [Google Scholar] [CrossRef]
  38. Friedrich, F.W.; Reischmann, S.; Schwalm, A.; Unger, A.; Ramanujam, D.; Münch, J.; Müller, O.J.; Hengstenberg, C.; Galve, E.; Charron, P.; et al. FHL2 expression and variants in hypertrophic cardiomyopathy. Basic Res. Cardiol. 2014, 109, 451. [Google Scholar] [CrossRef]
  39. Clemente-Olivo, M.P.; Hernández-Quiles, M.; Sparrius, R.; van der Stoel, M.M.; Janssen, V.; Habibe, J.J.; van den Burg, J.; Jongejan, A.; Alcaraz-Sobrevals, P.; van Es, R.; et al. Early adipogenesis is repressed through the newly identified FHL2-NFAT5 signaling complex. Cell. Signal. 2023, 104, 110587. [Google Scholar] [CrossRef]
  40. Clemente-Olivo, M.P.; Habibe, J.J.; Vos, M.; Ottenhoff, R.; Jongejan, A.; Herrema, H.; Zelcer, N.; Kooijman, S.; Rensen, P.C.N.; van Raalte, D.H.; et al. Four-and-a-half LIM domain protein 2 (FHL2) deficiency protects mice from diet-induced obesity and high FHL2 expression marks human obesity. Metabolism 2021, 121, 154815. [Google Scholar] [CrossRef]
  41. Zhu, Y.; Wang, Y.; Wang, Y.; Zhao, G.; Wen, J.; Cui, H. Transcriptome analysis reveals steroid hormones biosynthesis pathway involved in abdominal fat deposition in broilers. J. Integr. Agr. 2024, 23, 3118–3128. [Google Scholar] [CrossRef]
  42. Mattioli, A.V. Sex-specific impacts of caffeine on body composition: Commentary on a retrospective cohort study. J. Int. Soc. Sports Nutr. 2025, 22, 2454633. [Google Scholar] [CrossRef] [PubMed]
  43. Sima, A.; Manolescu, D.C.; Bhat, P. Retinoids and retinoid-metabolic gene expression in mouse adipose tissues. Biochem. Cell Biol. 2011, 89, 578–584. [Google Scholar] [CrossRef] [PubMed]
  44. Haduch, A.; Bromek, E.; Kuban, W.; Daniel, W.A. The Engagement of Cytochrome P450 Enzymes in Tryptophan Metabolism. Metabolites 2023, 13, 629. [Google Scholar] [CrossRef]
  45. Berry, D.C.; DeSantis, D.; Soltanian, H.; Croniger, C.M.; Noy, N. Retinoic acid upregulates preadipocyte genes to block adipogenesis and suppress diet-induced obesity. Diabetes 2012, 61, 1112–1121. [Google Scholar] [CrossRef]
  46. Lin, W.; Chow, S.K.H.; Cui, C.; Liu, C.; Wang, Q.; Chai, S.; Wong, R.M.Y.; Zhang, N.; Cheung, W.H. Wnt/beta-catenin signaling pathway as an important mediator in muscle and bone crosstalk: A systematic review. J. Orthop. Translat. 2024, 47, 63–73. [Google Scholar] [CrossRef]
  47. Zhang, X.; He, L.; Wang, L.; Wang, Y.; Yan, E.; Wan, B.; Zeng, Q.; Zhang, P.; Zhao, X.; Yin, J. CLIC5 promotes myoblast differentiation and skeletal muscle regeneration via the BGN-mediated canonical Wnt/β-catenin signaling pathway. Sci. Adv. 2024, 10, eadq6795. [Google Scholar] [CrossRef]
  48. Sinanoglou, V.J.; Mantis, F.; Miniadis-Meimaroglou, S.; Symeon, G.K.; Bizelis, I.A. Effects of caponisation on lipid and fatty acid composition of intramuscular and abdominal fat of medium-growth broilers. Br. Poult. Sci. 2011, 52, 310–317. [Google Scholar] [CrossRef]
  49. Zhang, Z.; Liao, Q.; Sun, Y.; Pan, T.; Liu, S.; Miao, W.; Li, Y.; Zhou, L.; Xu, G. Lipidomic and Transcriptomic Analysis of the Longissimus Muscle of Luchuan and Duroc Pigs. Front. Nutr. 2021, 8, 667622. [Google Scholar] [CrossRef]
  50. Wigger, D.; Schumacher, F.; Schneider-Schaulies, S.; Kleuser, B. Sphingosine 1-phosphate metabolism and insulin signaling. Cell. Signal. 2021, 82, 109959. [Google Scholar] [CrossRef]
  51. Tang, Y.; Yin, L.; Liu, L.; Chen, Q.; Lin, Z.; Zhang, D.; Wang, Y.; Liu, Y. Comparative Analysis of Different Proteins and Metabolites in the Liver and Ovary of Local Breeds of Chicken and Commercial Chickens in the Later Laying Period. Int. J. Mol. Sci. 2023, 24, 14394. [Google Scholar] [CrossRef]
Animals 15 03342 g001
Figure 1. Proportion of genetic variances of four fat traits explained by 0.05 Mb windows. AFW: abdominal fat weight; AFP abdominal fat percentage; IFPM: intramuscular fat of pectoral muscle; IFLM: intramuscular fat of leg muscle.
Figure 1. Proportion of genetic variances of four fat traits explained by 0.05 Mb windows. AFW: abdominal fat weight; AFP abdominal fat percentage; IFPM: intramuscular fat of pectoral muscle; IFLM: intramuscular fat of leg muscle.
Animals 15 03342 g001
Table 1. Descriptive statistics of abdominal fat percentage in Huainan Yellow chickens.
Table 1. Descriptive statistics of abdominal fat percentage in Huainan Yellow chickens.
Traits aNbMaxMinMeanSDCV (%)
CW21126068601615.27291.9518.07
LVW21184.0221.8835.599.3326.22
AFW211122.914.1853.7927.8851.82
HEW21116.794.658.992.3826.51
LVR2116.261.372.260.6830.07
AFP2110.846.43.181.4645.89
HER2110.960.320.560.1119.15
IFPM21110.1581.094.191.1527.54
IFLM21116.383.397.482.1528.75
a CW: carcass weight; LVW: liver weight; AFW: abdominal fat weight; HEW: heart weight; LVR: liver to body ratio; AFP abdominal fat percentage; HER: heart to body ratio; IFPM: intramuscular fat of pectoral muscle; IFLM: intramuscular fat of leg muscle.
Table 2. The phenotypic above diagonal and genetic below diagonal correlation between nine carcass traits.
Table 2. The phenotypic above diagonal and genetic below diagonal correlation between nine carcass traits.
CWLVWAFWHEWLVRAFPHERIFPMIFLM
CW10.281 **0.341 **0.701 **−0.402 **0.264 **−0.031−0.0970.085
LVW 1−0.1260.347 **0.743 **−0.167 *0.207 **0.169 *0.007
AFW 1−0.026−0.331 **0.989 **−0.337 **0.0930.276 **
HEW 1−0.133−0.0830.875 **−0.0640.039
LVR 1−0.329 **0.240 **0.242 **−0.047
AFP 1−0.345 **0.1020.275 **
HER 10.008−0.011
IFPM 10.157 *
IFLM 1
CW: carcass weight; LVW: liver weight; AFW: abdominal fat weight; HEW: heart weight; LVR: liver to body ratio; AFP abdominal fat percentage; HER: heart to body ratio; IFPM: intramuscular fat of pectoral muscle; IFLM: intramuscular fat of leg muscle. ** p < 0.01, and * p < 0.05.
Table 3. Significant SNPs and candidate genes associated with AFW, AFP, IFPM, and IFLM traits.
Table 3. Significant SNPs and candidate genes associated with AFW, AFP, IFPM, and IFLM traits.
TraitsChrnSNPPosition
(Mb)		nGeneEffect aGene
AFW11109,198,052414.75PRDM15, C2CD2, ZBTB21, UMODL
2151,692,642311.83BLVRA, VOPP1, LANCL2
2166,761,7821−7.05GMDS
10166,761,78216−12.54SCAMP2, ULK3, CPLX3, CSK, CYP1A2, CYP1A1, EDC3, CLK3, ARID3B, ACTG1L, UBL7, SEMA7A, CYP11A1,
STRA6, ISLR, ISLR2
1312,535,341326.69FAM114A2, GRIA1, MFAP3
35111,945,109130.32PA28_beta
AFP11112,969,66130.87OTC, TSPAN7, RPGR
715,897,98240.76TRAF3IP1, USP40, UGT1A1, SH3BP4
9121,433,493-−0.63-
10113,199,6696−1.75ACAN, AEN, DET1, RPS11, NUDIX, NTRK3
13111,945,10931.93FAM114A2, GRIA1, MFAP3
IFLM11149,287,395-2.76-
2179,956,86116−1.31OTC, RPGR, NUDIX, NTRK3, UGT1A1, TSPAN7,
MFAP3, ACAN, AEN, DET1, FAM114A2, SH3BP4,
RPS11, USP40, TRAF3IP1, NUDIX
21144,069,82311.33KCNK9, TRAPPC9
4189,896,431-0.78-
5122,865,61732.35CKAP5, LRP4, C11orf49
5128,039,56910.98RAD51B
25122,865,617200.66UBE2Q1, CHRNB2, ADAR, KCNN3, PBXIP1, PYGO2,
SHC1, CKS1B, FLAD1, LOC112530287, ZBTB7B, HCN3,
KHDC4, DCST2, LOC107050229, LOC107049672, FDPS,
SCAMP3, CLK2, ASH1L
IFPM1125,946,497-1.18-
11135,236,33541.03MRPS9, TGFBRAP1, C2orf49, FHL2
6129,803,88141.33VAX1, KCNK18, HSPA12A, SHTN1
9122,557,61661.38VEPH1, GMPS, LEKR1, TIPARP, SSR3, KCNAB1
12113,972,29030.63PRICKLE2, ADAMTS9, CCNL1
2311,059,92630.28ZCCHC17, FABP3, SERINC2
2613,635,01090.24LRIG2, MAGI3, TAFA3, WNT2B, ST7L, CAPZA1,
MOV10, RHOC, PPM1J
2811,544,929151.10UNC13A, MYO5B, PLPP2, LOC100857637, NFIC, FZR1,
LOC100858505, PIP5K1C, TBXA2R, HMG20B, DOHH, MFSD12, LOC101748203, C19orf71, CACTIN
a Positive values indicated an increase in the trait value of the effect allele; negative values indicated a decrease in the trait value of the effect allele.
Table 4. KEGG pathway enrichment analysis for candidate genes of AFW, AFP, IFPM, and IFLM.
Table 4. KEGG pathway enrichment analysis for candidate genes of AFW, AFP, IFPM, and IFLM.
TraitsIDDescriptionp-ValueKey Genes
AFWko00232Caffeine metabolism 4.29 × 104CYP1A2, CYP1A1
ko00140Steroid hormone biosynthesis 1.19 × 103CYP1A2, CYP1A1, CYP11A1
ko04913Ovarian steroidogenesis 1.87 × 103CYP1A2, CYP1A1, CYP11A1
ko00591Linoleic acid metabolism 8.05 × 103CYP1A2, CYP1A1
ko00830Retinol metabolism 1.32 × 102CYP1A2, CYP1A1
ko00980Metabolism of xenobiotics by cytochrome P4501.33 × 102CYP1A2, CYP1A1
ko00982Drug metabolism-cytochrome P4501.33 × 102CYP1A2, CYP1A1
ko00380Tryptophan metabolism1.57 × 102CYP1A2, CYP1A1
ko05204Chemical carcinogenesis-DNA adducts2.03 × 102CYP1A2, CYP1A1
AFPko00220Arginine biosynthesis 1.66 × 102OTC
ko00053Ascorbate and aldarate metabolism1.76 × 102UGT1A1
ko00040Pentose and glucuronate interconversions2.17 × 102UGT1A1
ko00860Porphyrin metabolism 2.47 × 102UGT1A1
ko05033Nicotine addiction 4.19 × 102GRIA1
ko00830Retinol metabolism 4.49 × 102UGT1A1
IJPMko04011MAPK signaling pathway-yeast6.72 × 103RHOC, PIP5K1C
ko04310Wnt signaling pathway9.21 × 103PRICKLE2, WNT2B, RHOC
ko04072Phospholipase D signaling pathway1.36 × 102PLPP2, RHOC, PIP5K1C
ko04144Endocytosis4.12 × 102CAPZA1, RHOC, PIP5K1C
ko04666Fc gamma R-mediated phagocytosis4.24 × 102PLPP2, PIP5K1C
ko05231Choline metabolism in cancer431 × 102PLPP2, PIP5K1C
IJLMko00511Other glycan degradation2.631 × 105LOC107050229, SCAMP3
ko00600Sphingolipid metabolism4.01 × 104LOC107050229, SCAMP3
ko00900Terpenoid backbone biosynthesis1.35 × 103FDPS
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Dai, Z.; Li, Y.; Liu, J.; Chen, R.; Zhu, H.; Lei, M. Genome-Wide Association Study of Abdominal and Intramuscular Fat Deposition Traits in Huainan Yellow-Feathered Chickens. Animals 2025, 15, 3342. https://doi.org/10.3390/ani15223342

AMA Style

Dai Z, Li Y, Liu J, Chen R, Zhu H, Lei M. Genome-Wide Association Study of Abdominal and Intramuscular Fat Deposition Traits in Huainan Yellow-Feathered Chickens. Animals. 2025; 15(22):3342. https://doi.org/10.3390/ani15223342

Chicago/Turabian Style

Dai, Zichun, Yaxin Li, Jie Liu, Rong Chen, Huanxi Zhu, and Mingming Lei. 2025. "Genome-Wide Association Study of Abdominal and Intramuscular Fat Deposition Traits in Huainan Yellow-Feathered Chickens" Animals 15, no. 22: 3342. https://doi.org/10.3390/ani15223342

APA Style

Dai, Z., Li, Y., Liu, J., Chen, R., Zhu, H., & Lei, M. (2025). Genome-Wide Association Study of Abdominal and Intramuscular Fat Deposition Traits in Huainan Yellow-Feathered Chickens. Animals, 15(22), 3342. https://doi.org/10.3390/ani15223342

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