通过加权基因共表达网络分析揭示影响猪脂肪沉积的候选基因

doi:10.3864/j.issn.0578-1752.2025.09.013

摘要/Abstract

摘要：

【目的】脂肪作为猪体内的主要储能组织，其沉积量不仅对养殖经济效益有着直接影响，还与猪肉品质密切相关。通过基于RNA测序数据和脂肪沉积相关性状的表型数据构建基因共表达网络，从而挖掘影响猪脂肪沉积的关键候选基因,探讨猪脂肪沉积的潜在调控机制。【方法】对28头杜洛克猪的背最长肌样本进行RNA测序，同时测定并计算了背膘厚、肥肉率、肌内脂肪（intramuscular fat, IMF）含量、身体质量指数（body mass index, BMI）等多项与脂肪沉积密切相关性状的表型数据；在此基础上，利用R语言WGCNA软件包开展加权基因共表达网络分析（weighted gene co-expression network analysis, WGCNA），从与脂肪沉积相关的共表达模块中筛选影响脂肪沉积的关键基因。【结果】通过WGCNA分析，共鉴别到28个基因共表达模块，其中Cyan模块和Purple模块与至少2个脂肪沉积相关性状呈显著相关（|相关系数|>0.3，显著性值>0.25）。这2个模块内基因功能富集分析结果显示，Cyan模块内的基因显著富集在脂肪酸生物合成（矫正后P = 3.48×10^-2）、鞘糖脂生物合成（矫正后P = 4.40×10^-2）等与脂肪沉积相关的通路中；但Purple模块基因未显著富集在任何与脂肪沉积相关的通路和GO项中（矫正后P >0.05）。另外，基于模块内连接性大于2、基因表达量与模块特征值的相关系数绝对值大于0.8、基因表达量与至少两个脂肪沉积相关表型的相关系数绝对值大于0.3的筛选标准，在与脂肪沉积相关模块内的92个基因中共鉴别到24个核心基因；其中，核心基因BET1L、NAGLU、B3GALT4和TMEM115的表达量与背膘厚、肥肉率、IMF含量和BMI等的相关系数均大于0.3，且基因功能注释结果发现这些基因的功能与脂肪沉积密切相关，表明这些基因可能在脂肪形成过程中发挥着重要作用。【结论】本研究在杜洛克猪中通过WGCNA鉴别到1个与脂肪沉积相关的关键模块，并在该模块中筛选出BET1L、NAGLU、B3GALT4和TMEM115等影响脂肪沉积的潜在候选基因。这些发现不仅加深了对猪脂肪沉积遗传因素的认识，也为进一步探讨猪脂肪沉积的潜在调控机制奠定了坚实的理论基础。

关键词: 猪, 脂肪沉积, WGCNA, RNA测序

Abstract:

【Objective】 Fat deposition is the major energy storage tissue in pigs, and its amount not only has a direct impact on the economic benefits of pig production, but also is closely related to the quality of pork. In this study, based on RNA sequencing data and phenotypic data of fat deposition related traits, a gene co-expression network was constructed to mine key candidate genes affecting pig fat deposition and to explore the potential regulatory mechanism of pig fat deposition. 【Method】 RNA sequencing was performed on the longissimus dorsi samples of 28 Duroc pigs. Phenotypic data of these pigs regarding fat deposition-related traits were measured and calculated, including backfat thickness, fat percentage, intramuscular fat (IMF) content, and body mass index (BMI). Weighted gene co-expression network analysis (WGCNA) was conducted using an R language WGCNA package based on the RNA sequencing data and these phenotypic data to identify critical genes from co-expression modules related to fat deposition. 【Result】 WGCNA identified a total of 28 co-expression modules, among which Cyan and Purple modules were strongly correlated with at least two fat deposition-related traits based on the criteria of |module-trait relationships| >0.3 and module gene significance >0.25. Functional enrichment analysis revealed that genes in Cyan module were significantly enriched in fat deposition-related pathways, such as fatty acids biosynthesis (adjusted P value = 3.48E-02) and glycosphingolipid biosynthesis (adjusted P value = 4.40E-02). In contrast, those genes in Purple module were not significantly enriched in any fat deposition-related pathways and GO terms (adjusted P value >0.05). Furthermore, combining the criteria of intra-modular connectivity greater than 0.2, an absolute correlation coefficient of gene expression with module eigengene exceeding 0.8, and the absolute correlation coefficient of gene expression with at least two fat deposition-related traits greater than 0.3, 24 hub genes of 92 genes in this fat deposition-related module were identified. Among these hub genes, the correlation coefficients between the expression of four genes, including BET1L, NAGLU, B3GALT4, and TMEM115, and four fat deposition-related traits, backfat thickness, fat percentage, IMF content, and BMI, were all greater than 0.3. Moreover, gene function annotation showed that the biological function of these genes were closely related to fat deposition. These results indicated that the four genes might play essential roles in fat deposition. 【Conclusion】 In this study, WGCNA was applied to Duroc pigs, resulting in the discovering a co-expression module closely associated with fat deposition-related traits. Within this module, four potential candidate genes affecting fat deposition were identified, namely BET1L, NAGLU, B3GALT4, and TMEM115. These findings not only deepened our understanding of the genetic factors involved in fat deposition, but also provided a solid theoretical reference for further exploration of the underlying mechanisms of fat deposition in pigs.

Key words: pig, fat deposition, WGCNA, RNA sequencing

王继英, 李菁璇, 王彦平, 郭建凤, 蔺海朝, 赵雪艳. 通过加权基因共表达网络分析揭示影响猪脂肪沉积的候选基因[J]. 中国农业科学, 2025, 58(9): 1845-1855.

WANG JiYing, LI JingXuan, WANG YanPing, GUO JianFeng, LIN HaiChao, ZHAO XueYan. Weighted Gene Co-Expression Network Analysis Reveals Potential Candidate Genes Affecting Fat Deposition in Pigs[J]. Scientia Agricultura Sinica, 2025, 58(9): 1845-1855.

0
/ / 推荐

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: https://www.chinaagrisci.com/CN/10.3864/j.issn.0578-1752.2025.09.013

https://www.chinaagrisci.com/CN/Y2025/V58/I9/1845

图/表 6

表1

图1

图2

图3

表2

Cyan模块内影响脂肪沉积的核心基因"

基因名称 Gene name	\|模块归属\| \|Module membership\|	连接性 Intra-modular connectivity	\|基因显著性值\| \|Gene significance\|, \|GS\|				基因功能 Gene function
基因名称 Gene name	\|模块归属\| \|Module membership\|	连接性 Intra-modular connectivity	背膘 Backfat thickness	肥肉率 Fat percentage	肌内脂肪 Intramuscular fat (IMF)	身体质量指数 Body mass index (BMI)	基因功能 Gene function
BET1L	0.91	5.61	0.34	0.46	0.32	0.34	促进高尔基囊泡膜的运输过程，具有可溶性N-乙基马来酰亚胺敏感因子附着蛋白受体（SNARE）活性 Promoting the transport of Golgi vesicles. Having a soluble N- ethylmaleimide-sensitive factor attachment protein receptor (SNARE) activity
NAGLU	0.84	4.14	0.47	0.44	0.30	0.35	参与硫酸乙酰肝素的代谢 Involved in the degradation of heparan sulfate
B3GALT4	0.84	3.94	0.45	0.48	0.34	0.38	参与GM1/GD1B/GA1神经节苷脂生物合成 Involved in GM1/GD1B/GA1 ganglioside biosynthesis
TMEM115	0.87	2.58	0.55	0.55	0.32	0.53	参与蛋白质从高尔基体向内质网的逆行运输 Involved in retrograde transport of proteins from the Golgi to the endoplasmic reticulum
SLC35A2	0.83	12.20	0.34	0.39	0.11	0.32	编码一种多通道膜蛋白，将半乳糖从胞浆中转运到高尔基体囊泡中，作为糖基供体用于生成糖基 This gene encodes a multi-pass membrane protein that transports UDP-galactose from the cytosol into Golgi vesicles, where it serves as a glycosyl donor for the generation of glycans
ENSSSCG00000038852	0.94	7.08	0.48	0.57	0.16	0.40
FRMD8	0.88	5.10	0.45	0.43	0.20	0.33	参与正向调节肿瘤坏死因子的产生 Involved in positive regulation of tumor necrosis factor production
SPPL2B	0.89	4.80	0.49	0.54	0.21	0.35	天冬氨酸蛋白酶GXGD家族的一个成员，触发固有和适应性免疫途径中细胞因子的表达 This gene encodes a member of the GXGD family of aspartic proteases, which triggers cytokine expression in the innate and adaptive immunity pathways
EXOSC6	0.93	3.74	0.43	0.42	0.17	0.44	编码外泌体复合物中一个亚基，参与多种细胞RNA加工和降解事件 This gene product constitutes one of the subunits of exosome. It is involved in a multitude of cellular RNA processing and degradation events
INTS5	0.85	3.47	0.30	0.41	0.54	0.26	编码整合因子复合体的亚基，参与mRNA合成调控和snRNA加工 This gene encodes a subunit of the Integrator complex. It is involved in the regulation of mRNA synthesis and snRNA processing
SURF6	0.81	3.40	0.32	0.38	0.16	0.33	编码具有核酸结合特性的一种核酸基质蛋白 This gene encodes a protein which may function as a nucleolar- matrix protein with nucleic acid-binding properties
MCM7	0.83	3.23	0.29	0.33	0.42	0.36	编码一种小染色体维持蛋白，对启动真核生物基因组复制至关重要 The protein encoded by this gene is a mini-chromosome maintenance protein (MCM) that is essential for the initiation of eukaryotic genome replication
RIOX1	0.82	2.51	0.39	0.39	0.10	0.37	编码一种加氧酶，可作为组蛋白赖氨酸去甲基化酶和核糖体组氨酸羟化酶 This gene encodes one kind of oxygenase, which can act as both a histone lysine demethylase and a ribosomal histidine hydroxylase
ENSSSCG00000034092	0.90	2.29	0.51	0.50	0.17	0.47
GPATCH3	0.80	2.14	0.28	0.39	0.35	0.37	参与转录调控 Involved in transcriptional regulation
FBXL12	0.84	3.65	0.42	0.53	0.23	0.26	F-box蛋白家族成员，作为蛋白质泛素连接酶 This gene encodes a member of the F-box protein family, which acts as a protein-ubiquitin ligase
COG8	0.82	3.41	0.22	0.34	0.15	0.34	高尔基寡聚体复合物 This gene encodes a protein that is a component of the conserved oligomeric Golgi (COG) complex
ENSSSCG00000040236	0.84	2.26	0.41	0.48	0.06	0.26

表2

图4

参考文献 43

[1]	POKLUKAR K, ČANDEK-POTOKAR M, BATOREK LUKAČ N, TOMAŽIN U, ŠKRLEP M. Lipid deposition and metabolism in local and modern pig breeds: A review. Animals, 2020, 10(3): 424.
[2]	SCHUMACHER M, DELCURTO-WYFFELS H, THOMSON J, BOLES J. Fat deposition and fat effects on meat quality: A review. Animals, 2022, 12(12): 1550.
[3]	TCHKONIA T, THOMOU T, ZHU Y, KARAGIANNIDES I, POTHOULAKIS C, JENSEN M D, KIRKLAND J L. Mechanisms and metabolic implications of regional differences among fat depots. Cell Metabolism, 2013, 17(5): 644-656. doi: 10.1016/j.cmet.2013.03.008 pmid: 23583168
[4]	GOTOH T, ALBRECHT E, TEUSCHER F, KAWABATA K, SAKASHITA K, IWAMOTO H, WEGNER J. Differences in muscle and fat accretion in Japanese Black and European cattle. Meat Science, 2009, 82(3): 300-308. doi: 10.1016/j.meatsci.2009.01.026 pmid: 20416730
[5]	JACYNO E, PIETRUSZKA A, KAWĘCKA M, BIEL W, KOŁODZIEJ- SKALSKA A. Phenotypic correlations of backfat thickness with meatiness traits, intramuscular fat, Longissimus muscle cholesterol and fatty acid composition in pigs. South African Journal of Animal Science, 2015, 45(2): 122.
[6]	吴建, 杨文, 孟孜, 查成万, 吴望军. 基于机器学习筛选猪活体肌内脂肪含量间接选育和构建预测模型的关键性状. 南京农业大学学报, 2025, 48(1): 161-170.
	WU J, YANG W, MENG Z, ZHA C W, WU W J. Screening of key traits for indirect selection and prediction machine learning in live pigs. Journal of Nanjing Agricultural University, 2025, 48(1): 161-170. (in Chinese)
[7]	LY N H, MAEKAWA T, YOSHIDA K, LIU Y, MURATANI M, ISHII S. RNA-sequencing analysis of paternal low-protein diet-induced gene expression change in mouse offspring adipocytes. G3 Genes\|Genomes\| Genetics, 2019, 9(7): 2161-2170.
[8]	GONG X L, ZHENG M, ZHANG J, YE Y R, DUAN M Q, CHAMBA Y, WANG Z B, SHANG P. Transcriptomics-based study of differentially expressed genes related to fat deposition in Tibetan and Yorkshire pigs. Frontiers in Veterinary Science, 2022, 9: 919904.
[9]	HOSSEINI S F, BAKHTIARIZADEH M R, SALEHI A. Meta- analysis of RNA-Seq datasets highlights novel genes/pathways involved in fat deposition in fat-tail of sheep. Frontiers in Veterinary Science, 2023, 10: 1159921.
[10]	XING S Y, LIU R R, ZHAO G P, LIU L, GROENEN M A M, MADSEN O, ZHENG M Q, YANG X T, CROOIJMANS R P M A, WEN J. RNA-seq analysis reveals hub genes involved in chicken intramuscular fat and abdominal fat deposition during development. Frontiers in Genetics, 2020, 11: 1009. doi: 10.3389/fgene.2020.01009 pmid: 33117416
[11]	ZHAO X, WANG C, WANG Y, ZHOU L, HU H, BAI L, WANG J. Weighted gene co-expression network analysis reveals potential candidate genes affecting drip loss in pork. Animal Genetics, 2020, 51(6): 855-865.
[12]	KADARMIDEEN H N, WATSON-HAIGH N S. Building gene co-expression networks using transcriptomics data for systems biology investigations: Comparison of methods using microarray data. Bioinformation, 2012, 8(18): 855-861. doi: 10.6026/97320630008855 pmid: 23144540
[13]	BONNET E, CALZONE L, MICHOEL T. Integrative multi-omics module network inference with lemon-tree. PLoS Computational Biology, 2015, 11(2): e1003983.
[14]	WANG J, YIN Y, LU Q, ZHAO Y R, HU Y J, HU Y Z, WANG Z Y. Identification of important modules and hub gene in chronic kidney disease based on WGCNA. Journal of Immunology Research, 2022, 2022: 4615292.
[15]	ZHONG Q L, JIN S Y, ZHANG Z B, QIAN H Y, XIE Y Q, YAN P L, HE W M, ZHANG L N. Identification and verification of circRNA biomarkers for coronary artery disease based on WGCNA and the LASSO algorithm. BMC Cardiovascular Disorders, 2024, 24(1): 305. doi: 10.1186/s12872-024-03972-2 pmid: 38880872
[16]	XING K, LIU H T, ZHANG F X, LIU Y B, SHI Y, DING X D, WANG C D. Identification of key genes affecting porcine fat deposition based on co-expression network analysis of weighted genes. Journal of Animal Science and Biotechnology, 2021, 12(1): 100. doi: 10.1186/s40104-021-00616-9 pmid: 34419151
[17]	CHEN C, REN H B, LI H L, DENG Y, CUI Q M, ZHU J, ZHANG S Y, YU J N, WANG H M, YU X D, YANG S L, HU X G, PENG Y L. Identification of crucial modules and genes associated with backfat tissue development by WGCNA in Ningxiang pigs. Frontiers in Genetics, 2023, 14: 1234757.
[18]	WANG H Y, WANG X Y, LI M L, WANG S Y, CHEN Q, LU S X. Identification of key sex-specific pathways and genes in the subcutaneous adipose tissue from pigs using WGCNA method. BMC Genomic Data, 2022, 23(1): 35. doi: 10.1186/s12863-022-01054-w pmid: 35538407
[19]	ZHAO X Y, HU H M, LIN H C, WANG C, WANG Y P, WANG J Y. Muscle transcriptome analysis reveals potential candidate genes and pathways affecting intramuscular fat content in pigs. Frontiers in Genetics, 2020, 11: 877. doi: 10.3389/fgene.2020.00877 pmid: 32849841
[20]	GONG H F, XIAO S J, LI W B, HUANG T, HUANG X C, YAN G R, HUANG Y Z, QIU H Q, JIANG K, WANG X P, et al. Unravelling the genetic loci for growth and carcass traits in Chinese Bamaxiang pigs based on a 1.4 million SNP array. Journal of Animal Breeding and Genetics, 2019, 136(1): 3-14. doi: 10.1111/jbg.12365 pmid: 30417949
[21]	LV Y M, XIE B B, BAI B J, SHAN L N, ZHENG W Q, HUANG X F, ZHU H B. Weighted gene coexpression analysis indicates that PLAGL2 and POFUT1 are related to the differential features of proximal and distal colorectal cancer. Oncology Reports, 2019, 42(6): 2473-2485. doi: 10.3892/or.2019.7368 pmid: 31638246
[22]	DRAGHICI S, KHATRI P, EKLUND A C, SZALLASI Z. Reliability and reproducibility issues in DNA microarray measurements. Trends in Genetics, 2006, 22(2): 101-109. doi: 10.1016/j.tig.2005.12.005 pmid: 16380191
[23]	徐炜琳, 李娜, 李兆华, 刘庆雨, 张琪, 金鑫, 张树敏. 松辽黑猪肌内脂肪含量与背膘厚的关联分析. 黑龙江畜牧兽医, 2017(22): 68-69.
	XU W L, LI N, LI Z H, LIU Q Y, ZHANG Q, JIN X, ZHANG S M. Association analysis between intramuscular fat content and back fat thickness of Songliao black pigs. Heilongjiang Animal Science and Veterinary Medicine, 2017(22): 68-69. (in Chinese)
[24]	孙义姗, 李梦寻, 闫冲, 李亚强, 黄涛. 猪背膘厚度与肌内脂肪含量的相关性研究. 猪业科学, 2019, 36(12): 96-98.
	SUN Y S, LI M X, YAN C, LI Y Q, HUANG T. Correlation analysis between back fat thickness and intramuscular fat content. Swine Industry Science, 2019, 36(12): 96-98. (in Chinese)
[25]	BAHELKA I, HANUSOVÁ E, PEŠKOVIČOVÁ D, DEMO P. The effect of sex and slaughter weight on intramuscular fat content and its relationship to carcass traits of pigs. Czech Journal of Animal Science, 2007, 52(5): 122-129.
[26]	郭永春, 王鹏杰, 金珊, 侯炳豪, 王淑燕, 赵峰, 叶乃兴. 基于WGCNA鉴定茶树响应草甘膦相关的基因共表达模块. 中国农业科学, 2022, 55(1): 152-166. doi: 10.3864/j.issn.0578-1752.2022.01.013.
	GUO Y C, WANG P J, JIN S, HOU B H, WANG S Y, ZHAO F, YE N X. Identification of co-expression gene related to tea plant response to glyphosate based on WGCNA. Scientia Agricultura Sinica, 2022, 55(1): 152-166. doi: 10.3864/j.issn.0578-1752.2022.01.013. (in Chinese)
[27]	陈华峰, 田可川, 黄锡霞, 阿布来提·苏来曼, 何军敏, 田月珍, 徐新明, 付雪峰, 赵冰茹, 朱桦, 哈尼克孜·吐拉甫. 苏博美利奴羊毛囊发育相关lncRNA与mRNA共表达网络的构建. 中国农业科学, 2019, 52(19): 3471-3484. doi: 10.3864/j.issn.0578-1752.2019.19.016.
	CHEN H F, TIAN K C, HUANG X X, ABLAT SULAYMAN, HE J M, TIAN Y Z, XU X M, FU X F, ZHAO B R, ZHU H, HANIKEZI TULAFU. Construction of co-expression network of lncRNA and mRNA related to hair follicle development of subo merino sheep. Scientia Agricultura Sinica, 2019, 52(19): 3471-3484. doi: 10.3864/j.issn.0578-1752.2019.19.016. (in Chinese)
[28]	STINKENS R, GOOSSENS G H, JOCKEN J W E, BLAAK E E. Targeting fatty acid metabolism to improve glucose metabolism. Obesity Reviews, 2015, 16(9): 715-757. doi: 10.1111/obr.12298 pmid: 26179344
[29]	LOPEZ P H, SCHNAAR R L. Gangliosides in cell recognition and membrane protein regulation. Current Opinion in Structural Biology, 2009, 19(5): 549-557. doi: 10.1016/j.sbi.2009.06.001 pmid: 19608407
[30]	VAN EIJK M, ATEN J, BIJL N, OTTENHOFF R, VAN ROOMEN C P A A, DUBBELHUIS P F, SEEMAN I, GHAUHARALI-VAN DER VLUGT K, OVERKLEEFT H S, ARBEENY C, GROEN A K, AERTS J M F G. Reducing glycosphingolipid content in adipose tissue of obese mice restores insulin sensitivity, adipogenesis and reduces inflammation. PLoS ONE, 2009, 4(3): e4723.
[31]	LIPINA C, HUNDAL H S. Ganglioside GM3 as a gatekeeper of obesity-associated insulin resistance: Evidence and mechanisms. FEBS Letters, 2015, 589(21): 3221-3227. doi: 10.1016/j.febslet.2015.09.018 pmid: 26434718
[32]	MALSAM J, SOLLNER T H. Organization of SNAREs within the Golgi stack. Cold Spring Harbor Perspectives in Biology, 2011, 3(10): a005249.
[33]	NEWMAN A P, SHIM J, FERRO-NOVICK S. BET1 BOS1 and SEC22 are members of a group of interacting yeast genes required for transport from the endoplasmic reticulum to the Golgi complex. Molecular and Cellular Biology, 1990, 10(7): 3405-3414.
[34]	THURMOND D C. Regulation of insulin action and insulin secretion by SNARE-mediated vesicle exocytosis. Mechanisms of Insulin Action. New York, NY: Springer New York, 2007: 52-70.
[35]	YU CHEN H, DINA C, SMALL A M, SHAFFER C M, LEVINSON R T, HELGADóTTIR A, CAPOULADE R, MUNTER H M, MARTINSSON A, CAIRNS B J. Dyslipidemia, inflammation, calcification, and adiposity in aortic stenosis: A genome-wide study. European Heart Journal, 2023, 44(21): 1927-1939. doi: 10.1093/eurheartj/ehad142 pmid: 37038246
[36]	GORDTS P L S M, ESKO J D. Heparan sulfate proteoglycans fine-tune macrophage inflammation via IFN-β. Cytokine, 2015, 72(1): 118-119.
[37]	JOSEPH S, ZBIDAT S, VOLODIN A, KASIVISWANATHAN D, FRIED A I, ARMANI A, GILDA J E, COHEN S. Insulin receptor turnover in fasting is dependent on β-dystroglycan deglycosylation. BioRxiv, 2023: 2022.2006.2024. 497215.
[38]	FU H Y, MEADOWS A S, JASON DUNCAN F, MCCARTY D M. 172. correction of profound metabolic impairments in MPS IIIB mice by a systemic rAAV9-hNAGLU gene delivery. Molecular Therapy, 2015, 23: S69.
[39]	SASAKI N, ITAKURA Y, TOYODA M. Ganglioside GM1 contributes to the state of insulin resistance in senescent human arterial endothelial cells. Journal of Biological Chemistry, 2015, 290(42): 25475-25486. doi: 10.1074/jbc.M115.684274 pmid: 26338710
[40]	LINKE V, OVERMYER K A, MILLER I J, BRADEMAN D R, HUTCHINS P D, TRUJILLO E A, REDDY T R, RUSSELL J D, CUSHING E M, SCHUELER K L, STAPLETON D S, RABAGLIA M E, KELLER M P, GATTI D M, KEELE G R, PHAM D, BROMAN K W, CHURCHILL G A, ATTIE A D, COON J J. A large-scale genome-lipid association map guides lipid identification. Nature Metabolism, 2020, 2(10): 1149-1162. doi: 10.1038/s42255-020-00278-3 pmid: 32958938
[41]	SHEN X. Characterising the rhomboid-like protein TMEM115. University of Oxford, 2017.
[42]	ENGIN A. Adipose tissue hypoxia in obesity and its impact on preadipocytes and macrophages:Hypoxia hypothesis. Obesity and Lipotoxicity. Cham: Springer International Publishing, 2017: 305-326.
[43]	HUBBI M E, LUO W B, BAEK J H, SEMENZA G L. MCM proteins are negative regulators of hypoxia-inducible factor 1. Molecular Cell, 2011, 42(5): 700-712. doi: 10.1016/j.molcel.2011.03.029 pmid: 21658608

	背膘厚 Backfat thickness (mm)	肥肉率 Fat percentage (%)	肌内脂肪含量 Intramuscular fat (IMF) content (%)	身体质量指数 Body mass index (BMI)
平均值 Mean	16.26	15.61	2.36	84.96
标准差 Standard deviation	4.49	3.11	0.71	6.00
变异系数 Coefficient of variation (%)	27.71	20.13	29.75	6.42
最大值 Maximum	24.30	22.80	4.23	95.43
最小值 Minimum	7.60	10.60	1.17	74.49