太行鸡与坝上长尾鸡品种区分的分子标记筛选与鉴定

doi:10.11843/j.issn.0366-6964.2025.08.018

Abstract

Abstract:

The aim was to distinguish between Taihang chickens and Bashang long-tailed chickens using a small number of SNPs by screening characteristic SNPs molecular markers. The whole genome resequencing data from 61 Taihang and 56 Bashang long-tailed chickens were used to extract important SNPs through linkage disequilibrium and population differentiation index analyses. Six machine learning classification models were adopted to predict individual categories and evaluate the model performance. The random forest model was used to evaluate the importance of SNPs, and the minimum number of SNPs were selected according to the accuracy, recall rate and AUC value. The effect was validated using principal component analysis, phylogenetic tree, and a genomic relationship matrix. A total of 28 SNPs were identified that effectively discriminate between Taihang and Bashang long-tailed chickens. The SNPs were primarily located in intergenic and intronic regions and were significantly enriched in the GO biological processes related to immunity, histone acetylation, metabolism as well as in the KEGG signaling pathway for base excision repair. Additionally, we identified genes associated with immunity (BCL11B, AvBD13, KAT7), lipid metabolism (URI1, RREB1, ZBTB20), and adaptability (ZNF536). The identified molecular marker combinations with integration of population genetics and machine learning techniques could distinguish Taihang and Bashang long-tailed chicken breeds. This study provides valuable insights for the development of "molecular identity cards" for local breeds, contributing to the conservation and identification of germplasm resources.

Key words: Taihang chicken, Bashang long-tailed chicken, molecular marker, machine learning

CLC Number:

S831.2

FU Wei, ZHANG Ran, DING Hong, ZANG Sumin, LI Xianglong, CHU Suqiao, LIU Huage, ZHOU Rongyan. Screening and Identification of Molecular Markers for Differentiating Taihang Chickens and Bashang Long-tailed Chickens[J]. Acta Veterinaria et Zootechnica Sinica, 2025, 56(8): 3761-3772.

Figures/Tables 10

Table 1

Fig. 1

Table 2

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Table 3

Fig. 6

Fig. 7

References 47

1	关琛, 王茂森. 农业文化遗产视角下坝上长尾鸡养殖系统的价值及保护利用研究[J]. 古今农业, 2022 (2): 106-113, 65. doi: 10.3969/j.issn.1672-2787.2022.02.012
	GUAN C , WANG M S . Study on the value, protection and utilization of Bashang long-tailed chicken breeding system from the perspective of agricultural cultural heritage[J]. Ancient and Modern Agriculture, 2022 (2): 106-113, 65. doi: 10.3969/j.issn.1672-2787.2022.02.012
2	魏萌, 谢鹏, 陈润梓, 等. 太行鸡肉蛋品质研究进展[J]. 北方牧业, 2023 (23): 31.
	WEI M , XIE P , CHEN R Z , et al. Research progress of Taihang chicken egg quality[J]. Northern Animal Husbandry, 2023 (23): 31.
3	杨光华, 赵汝苍. 地方畜禽遗传资源保护与利用的现状及对策[J]. 农村科学实验, 2024 (6): 151- 153.
	YANG G H , ZHAO R C . Current situation and countermeasures of protection and utilization of local livestock and poultry genetic resources[J]. Rural Scientific Experiment, 2024 (6): 151- 153.
4	于福清. 加强畜禽遗传资源保护夯实畜禽种业振兴根基[J]. 农村工作通讯, 2022 (16): 26- 28. doi: 10.3969/j.issn.0546-9503.2022.16.013
	YU F Q . Strengthen the protection of livestock and poultry genetic resources to consolidate the foundation of revitalization of livestock and poultry seed industry[J]. Rural Work Communication, 2022 (16): 26- 28. doi: 10.3969/j.issn.0546-9503.2022.16.013
5	RASHEED A , HAO Y F , XIA X C , et al. Crop breeding chips and genotyping platforms: progress, challenges, and perspectives[J]. Mol Plant, 2017, 10 (8): 1047- 1064. doi: 10.1016/j.molp.2017.06.008
6	白露, 王梦杰, 马小春, 等. 一种快速挖掘鸡品种特征性SNP标记集合的方法[J]. 畜牧兽医学报, 2023, 54 (8): 3252- 3261. doi: 10.11843/j.issn.0366-6964.2023.08.012
	BAI L , WANG M J , MA X C , et al. A method for quickly mining the characteristic SNP markers set of chicken[J]. Acta Veterinaria et Zootechnica Sinica, 2023, 54 (8): 3252- 3261. doi: 10.11843/j.issn.0366-6964.2023.08.012
7	CHOI R Y , COYNER A S , KALPATHY-CRAMER J , et al. Introduction to machine learning, neural networks, and deep learning[J]. Transl Vis Sci Technol, 2020, 9 (2): 14.
8	陈志强, 梁齐齐, 吴俊, 等. 品种鉴定的方法、其预测模型的构建方法和装置[P]. 北京: CN202011120761.2, 2024-01-30.
	CHEN Z Q, LIANG Q Q, WU J, et al. The method of variety identification, the method and device of its prediction model construction[P]. Beijing: CN202011120761.2, 2024-01-30. (in Chinese)
9	刘杰, 周艳, 王杰, 等. 一种莱芜黑鸡鉴定的SNP分子标记及应用[P]. 山东: CN202310513437.4, 2023-09-29.
	LIU J, ZHOU Y, WANG J, et al. A SNP molecular marker for the identification of Laiwu black chicken and its application[P]. Shandong: CN202310513437.4, 2023-09-29. (in Chinese)
10	SCHIAVO G , BERTOLINI F , BOVO S , et al. Identification of population-informative markers from high-density genotyping data through combined feature selection and machine learning algorithms: application to European autochthonous and cosmopolitan pig breeds[J]. Anim Genet, 2024, 55 (2): 193- 205. doi: 10.1111/age.13396
11	SEO D , CHO S , MANJULA P , et al. Identification of target chicken populations by machine learning models using the minimum number of SNPs[J]. Animals (Basel), 2021, 11 (1): 241.
12	ZHI Y H , GENG W Z , LI S H , et al. Advanced molecular system for accurate identification of chicken genetic resources[J]. Comput Electron Agric, 2025, 231, 109989.
13	GAO J , SUN L W , ZHANG S S , et al. Screening discriminating SNPs for Chinese indigenous pig breeds identification using a random forests algorithm[J]. Genes (Basel), 2022, 13 (12): 2207. doi: 10.3390/genes13122207
14	陈艳平. 太行鸡不同饲养模式的比较研究[D]. 邯郸: 河北工程大学, 2023.
	CHEN Y P. Comparative study on different feeding modes of Taihang chickens[D]. Handan: Hebei University of Engineering, 2023. (in Chinese)
15	郭连伟. 地方特色品种——坝上长尾鸡[J]. 河北农业, 2020 (7): 34- 35.
	GUO L W . Local characteristic breed-Bashang Long-tailed chicken[J]. Hebei Agriculture, 2020 (7): 34- 35.
16	QANBARI S . On the extent of linkage disequilibrium in the genome of farm animals[J]. Front Genet, 2020, 10, 1304.
17	ALCANTARA L M , SCHENKEL F S , LYNCH C , et al. Machine learning classification of breeding protocol descriptions from Canadian Holsteins[J]. J Dairy Sci, 2022, 105 (10): 8177- 8188.
18	GOODSWEN S J , BARRATT J L N , KENNEDY P J , et al. Machine learning and applications in microbiology[J]. FEMS Microbiol Rev, 2021, 45 (5): fuab015.
19	WILMOT H , NIEHOFF T , SOYEURT H , et al. The use of a genomic relationship matrix for breed assignment of cattle breeds: comparison and combination with a machine learning method[J]. J Anim Sci, 2023, 101, skad172.
20	张冬杰, 孙亚蒙, 汪亮, 等. FST基因的过表达对猪成纤维细胞内基因及生物学通路的影响[J]. 吉林农业大学学报, 2022, 44 (2): 198- 203.
	ZHANG D J , SUN Y M , WANG L , et al. Effects of FST gene overexpression on genes and biological pathways in porcine fibroblasts[J]. Journal of Jilin Agricultural University, 2022, 44 (2): 198- 203.
21	GAO C Q , WANG K J , HU X Y , et al. Conservation priority and run of homozygosity pattern assessment of global chicken genetic resources[J]. Poult Sci, 2023, 102 (11): 103030.
22	RACHMAN M P , BAMIDELE O , DESSIE T , et al. Genomic analysis of Nigerian indigenous chickens reveals their genetic diversity and adaptation to heat-stress[J]. Sci Rep, 2024, 14 (1): 2209.
23	LIANG H , WANG X , SI J F , et al. Classification accuracy of machine learning algorithms for Chinese local cattle breeds using genomic markers[J]. Yi Chuan, 2024, 46 (7): 530- 539.
24	MALOMANE D K , REIMER C , WEIGEND S , et al. Efficiency of different strategies to mitigate ascertainment bias when using SNP panels in diversity studies[J]. BMC Genomics, 2018, 19 (1): 22.
25	LI X L , WU Q M , ZHANG X X , et al. Whole-genome resequencing to study brucellosis susceptibility in sheep[J]. Front Genet, 2021, 12, 653927.
26	XUE Z , WANG L , TIAN Y M , et al. A genome-wide scan to identify signatures of selection in Lueyang black -bone chicken[J]. Poult Sci, 2023, 102 (7): 102721.
27	GREENER J G , KANDATHIL S M , MOFFAT L , et al. A guide to machine learning for biologists[J]. Nat Rev Mol Cell Biol, 2022, 23 (1): 40- 55.
28	ZHAO C H , WANG D , TENG J , et al. Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data[J]. J Anim Sci Biotechnol, 2023, 14 (1): 85.
29	CHO E , CHO S , KIM M , et al. Single nucleotide polymorphism marker combinations for classifying Yeonsan Ogye chicken using a machine learning approach[J]. J Anim Sci Technol, 2022, 64 (5): 830- 841.
30	XU N Y , SI W , LI M , et al. Genome-wide scan for selective footprints and genes related to cold tolerance in Chantecler chickens[J]. Zool Res, 2021, 42 (6): 710- 720.
31	ROMANOV M N , ABDELMANOVA A S , FISININ V I , et al. Selective footprints and genes relevant to cold adaptation and other phenotypic traits are unscrambled in the genomes of divergently selected chicken breeds[J]. J Anim Sci Biotechnol, 2023, 14 (1): 35.
32	FORKEL H , GRABARCZYK P , DEPKE M , et al. BCL11B depletion induces the development of highly cytotoxic innate T cells out of IL-15 stimulated peripheral blood αβ CD8+ T cells[J]. Oncoimmunology, 2022, 11 (1): 2148850.
33	QIU L L , YANG T , GUO Q X , et al. C2H2-type zinc-finger protein BCL11B suppresses avian Leukosis virus subgroup J replication by regulating apoptosis[J]. Int J Biol Macromol, 2024, 275 (1): 133644.
34	沈元朝. 金纳米探针暗场可视化检测鸡Bcl11b正相关circ_2420的研究[D]. 扬州: 扬州大学, 2023.
	SHEN Y C. Study on gold nanopartical probe mediated dark-field visual enumeration strategy for chicken Bcl11b positive associated circ_2420[D]. Yangzhou: Yangzhou University, 2023. (in Chinese)
35	李娟, 刘阳, 牛军波, 等. 禽类β-防御素的研究进展[J]. 中国家禽, 2019, 41 (2): 39- 43.
	LI J , LIU Y , NIU J B , et al. Progress on the study of AvBDs[J]. China Poultry, 2019, 41 (2): 39- 43.
36	杨玉荣, 佘锐萍, 梁宏德. 雏鸡服用AvBD13后体液免疫的动态变化[J]. 中国农业科学, 2009, 42 (10): 3679- 3684.
	YANG Y R , SHE R P , LIANG H D . Effects of AvBD13 on humoral immunity of chickens[J]. Scientia Agricultura Sinica, 2009, 42 (10): 3679- 3684.
37	FU Y J , ZHANG S J , ZHAO N , et al. Effect of mild intermittent cold stimulation on thymus immune function in broilers[J]. Poult Sci, 2022, 101 (10): 102073.
38	NEWMAN D M , VOSS A K , THOMAS T , et al. Essential role for the histone acetyltransferase KAT7 in T cell development, fitness, and survival[J]. J Leukoc Biol, 2017, 101 (4): 887- 892.
39	HEINLEIN M , GANDOLFO L C , ZHAO K , et al. The acetyltransferase KAT7 is required for thymic epithelial cell expansion, expression of AIRE target genes, and thymic tolerance[J]. Sci Immunol, 2022, 7 (67): eabb6032.
40	YEVSHIN I S , SHAGIMARDANOVA E I , RYABOVA A S , et al. Genome of Russian snow-white chicken reveals genetic features associated with adaptations to cold and diseases[J]. Int J Mol Sci, 2024, 25 (20): 11066.
41	ZHANG H , LIANG Q Q , WANG N , et al. Microevolutionary dynamics of chicken genomes under divergent selection for adiposity[J]. IScience, 2020, 23 (6): 101193.
42	YU G Z , KRENTZ N A J , BENTLEY L , et al. Loss of RREB1 reduces adipogenesis and improves insulin sensitivity in mouse and human adipocytes[J]. bioRxiv, 2024, 31, 2024.07.30.605923.
43	LIU G , ZHOU L T , ZHANG H , et al. Regulation of hepatic lipogenesis by the zinc finger protein Zbtb20[J]. Nat Commun, 2017, 8, 14824.
44	周露婷. 肝脏ZBTB20调节脂质代谢和胰岛素敏感性的机制研究[D]. 上海: 第二军医大学, 2013.
	ZHOU L T. Regulation of lipid metabolism and insulin sensitivity by hepatic zinc finger protein ZBTB20[D]. Shanghai: Naval Medical University, 2013. (in Chinese)
45	付雅杰. 冷刺激诱导冷适应过程中的脂质代谢[D]. 哈尔滨: 东北农业大学, 2023.
	FU Y J. Cold stimulation induces lipid metabolism during cold adaptation[D]. Harbin: Northeast Agricultural University, 2023. (in Chinese)
46	XIE S S , YANG X K , WANG D H , et al. Thyroid transcriptome analysis reveals different adaptive responses to cold environmental conditions between two chicken breeds[J]. PLoS One, 2018, 13 (1): e0191096.
47	刘慧芳, 倪慧勇, 郝静, 等. 太行鸡冬季暖棚生态放养模式[J]. 北方牧业, 2024 (6): 21.
	LIU H F , NI H Y , HAO J , et al. Winter greenhouse-based ecological husbandry system for Taihang chicken[J]. Northern Animal Husbandry, 2024 (6): 21.

性状 Trait	鸡种 Breed	周龄 Weeks of age
性状 Trait	鸡种 Breed	1	2	4	6	8	10	12	14	16	18	20
周末体重/g Weekend weight	太行鸡 Taihang chicken	58	84	159	328	473	647	786	914	1 058	1 184	1 239
周末体重/g Weekend weight	坝上长尾鸡 Bashang long-tailed chicken	62	125	212	336	453	596	756	924	1 150	1 264	1 409

模型 Model	AUC	MCC 值 MCC value	准确率 Accuracy
随机森林 Random forest	1.00	1.00	1.00
逻辑回归 Logistic regression	1.00	1.00	1.00
支持向量机 Support vector machine	1.00	1.00	1.00
极致梯度提升树 eXtreme gradient boosting	1.00	1.00	1.00
K 近邻算法 K-nearest neighbor classification	1.00	1.00	1.00
决策树 Decision tree	0.82	0.63	0.79

染色体 Chromosome	位置 Position	等位基因 Allele	基因 Gene	等位基因频率 Allele frequency
染色体 Chromosome	位置 Position	等位基因 Allele	基因 Gene	太行鸡 Taihang chicken	坝上长尾鸡 Bashang long-tailed chicken
1	83424842	G/A	ZBTB20	0.67/0.33	0.94/0.06
1	84642349	T/G	CD200R1、CCDC80	0.38/0.62	0.71/0.29
1	124613919	G/A	EGFL6	0.34/0.66	0.71/0.29
1	188821891	G/A	CHORDC1	0.69/0.31	0.29/0.71
2	21199573	C/T	ZNF804B	0.73/0.27	0.26/0.74
2	64783482	C/T	RREB1	0.52/0.48	0.86/0.14
3	52482379	C/T	PHACTR2	0.66/0.34	0.35/0.65
3	107385968	C/A	gga-mir-6702、AvBD13	0.65/0.35	0.34/0.66
4	83205244	G/A	-	0.71/0.29	0.31/0.69
4	89928184	A/G	EXOC6B	0.41/0.59	0.76/0.24
4	90119859	G/A	EXOC6B	0.50/0.50	0.83/0.17
5	48014977	C/A	BCL11B	0.51/0.49	0.81/0.19
6	3512624	A/G	PARG	0.34/0.66	0.65/0.35
6	23346870	G/A	SLIT1	0.74/0.26	0.22/0.78
8	4957820	A/G	DNM3	0.35/0.65	0.74/0.26
8	22102533	G/C	-	0.66/0.34	0.90/0.10
9	3411829	C/T	-	0.74/0.26	0.39/0.61
11	8491921	T/C	-	0.70/0.30	0.95/0.05
11	8532637	A/G	URI1	0.69/0.31	0.30/0.70
11	8851889	C/T	ZNF536	0.74/0.26	0.96/0.04
24	671698	T/C	KIRREL3	0.45/0.55	0.78/0.22
24	5374821	C/T	DSCAML1	0.32/0.68	0.74/0.26
25	3692518	A/G	MCL1	0.36/0.64	0.71/0.29
26	3849905	G/T	TRIM33	0.71/0.29	0.95/0.05
26	4170859	T/C	SCUBE3	0.32/0.68	0.70/0.30
27	5676121	A/C	KAT7	0.80/0.20	0.39/0.61
28	2536492	G/A	MBD3	0.67/0.33	0.33/0.67
33	5235050	G/A	-	0.66/0.34	0.93/0.07

[1]	DONG Jiaojiao, DING Hong, ZHANG Yinliang, ZHANG Ran, LIU Huage, ZANG Sumin, ZHANG Zhenhong, ZHOU Rongyan, LI Lanhui. Differences and Functional Analysis of Cecal Flora in Taihang Chickens Infected with Salmonella Pullorum [J]. Acta Veterinaria et Zootechnica Sinica, 2025, 56(6): 2741-2751.
[2]	QIAO Liying, WANG Wannian, ZHANG Li, PANG Zhixu, ZHANG Siying, LI Yifan, LIU Wenzhong. Machine Learning Methods for Sheep Breed Classification Based on Genomic Markers [J]. Acta Veterinaria et Zootechnica Sinica, 2025, 56(5): 2157-2167.
[3]	LI Xiaotong, WANG Pengyu, FANG Yingyan, YU Hongxi, ZHANG Yi, WANG Yachun, ZHANG Yuanpei, LI Yanqin, JIANG Li. Mining and Functional Verification of Gene Polymorphisms Loci Related to Bull Sperm Freezability [J]. Acta Veterinaria et Zootechnica Sinica, 2025, 56(4): 1981-1988.
[4]	YANG Miaomiao, XIE Li, JIAN Baoyi, LUO Chaowei, XIE Zhuojun, ZHU Piao, ZHOU Tianri, LI Hua, XIANG Hai. Construction and Optimization of Prediction Models for Abdominal Fat Deposition in Adult Hens based on Early Body Size Traits using Machine Learning [J]. Acta Veterinaria et Zootechnica Sinica, 2025, 56(2): 548-558.
[5]	LI Yuanfang, WU Ran, LI Shuaihao, WEI Qianran, WANG Yadong, WANG Dandan, LI Zhi, LI Guoxi, LIU Qiaoming. The Role of G3BP1 in the Proliferation and Differentiation of Chicken Intramuscular Preadipocytes and Identification of Its Molecular Markers [J]. Acta Veterinaria et Zootechnica Sinica, 2025, 56(1): 159-167.
[6]	YANG Wenpan, LIU Xiangjie, LUO Dongxiang, CHEN Menghui, XIE Ying, FANG Yuexin, LIN Tingyan, LI Aimin, LI Wenjing, DENG Zheng, DING Nengshui. Research on Genomic Selection of Reproductive Traits in Landrace Pigs Based on Chip Data [J]. Acta Veterinaria et Zootechnica Sinica, 2025, 56(1): 213-221.
[7]	Jinbu WANG, Jia LI, Deming REN, Lixian WANG, Ligang WANG. Progress in the Application of Machine Learning in Livestock and Poultry Genomic Selection [J]. Acta Veterinaria et Zootechnica Sinica, 2024, 55(7): 2775-2785.
[8]	Jing LI, Yuanxu ZHANG, Zezhao WANG, Yan CHEN, Lingyang XU, Lupei ZHANG, Xue GAO, Huijiang GAO, Junya LI, Bo ZHU, Peng GUO. Research Progress in Machine Learning Genomic Selection [J]. Acta Veterinaria et Zootechnica Sinica, 2024, 55(6): 2281-2292.
[9]	NIU Jiajia, XU Dan, LIU Yang, ZHAO Xiaoling. Research Progress on Genetic Regulation Mechanism of Barring Feather Trait in Chicken [J]. Acta Veterinaria et Zootechnica Sinica, 2024, 55(5): 1883-1892.
[10]	ZHANG Yuanxu, LI Jing, WANG Zezhao, CHEN Yan, XU Lingyang, ZHANG Lupei, GAO Xue, GAO Huijiang, LI Junya, ZHU Bo, GUO Peng. Advances in Animal Genetic Evaluation Software [J]. Acta Veterinaria et Zootechnica Sinica, 2024, 55(5): 1827-1841.
[11]	Shuhan YANG, Yuxin SHI, Yunwei PANG, Kaimin YUAN, Haoyu XIU, Chao WANG, Yongqiang LU, Dong WANG. Research Progress of Estrus Identification Markers in Cows [J]. Acta Veterinaria et Zootechnica Sinica, 2024, 55(11): 4785-4795.
[12]	CHU Chu, ZHANG Jingjing, DING Lei, FAN Yikai, BAO Xiangnan, XIANG Shixin, LIU Rui, LUO Xuelu, REN Xiaoli, LI Chunfang, LIU Wenju, WANG Liang, LIU Li, LI Yongqing, JIANG Han, LI Weiqi, SUN Wei, LI Xihe, WEN Wan, ZHOU Jiamin, ZHANG Shujun. Establishment and Application of Prediction Model of Three Amino Acids in Milk Based on Mid-infrared Spectroscopy [J]. Acta Veterinaria et Zootechnica Sinica, 2023, 54(8): 3299-3312.
[13]	LI Tingting, LIU Qiuyue, LI Xiangchen, WANG Haitao. Research Progress and Applications of Genes Associated with Economic Traits in Sheep [J]. Acta Veterinaria et Zootechnica Sinica, 2022, 53(8): 2417-2434.
[14]	DING Jiqiang, LI Qinghe, ZHANG Gaomeng, LI Sen, ZHENG Maiqing, WEN Jie, ZHAO Guiping. Comparing the Accuracy of Estimated Breeding Value by Several Algorithms on Laying Traits in Broilers [J]. Acta Veterinaria et Zootechnica Sinica, 2022, 53(5): 1364-1372.
[15]	ZHANG Beibei, LI Mengxiao, MA Tenghe, LI Xuenan, WEI Jiarong, KANG Guolei, WANG Hongna, LIU Chao, WANG Bin, SUN Yanyan. Cloning, Bioinformatics and Expression Analysis of FST Gene in Taihang Chickens [J]. Acta Veterinaria et Zootechnica Sinica, 2021, 52(11): 3064-3075.

Screening and Identification of Molecular Markers for Differentiating Taihang Chickens and Bashang Long-tailed Chickens

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 47

Related Articles 15

Recommended Articles

Metrics

Comments