IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i2p267-d1043496.html
   My bibliography  Save this article

Investigating Genetic Characteristics of Chinese Holstein Cow’s Milk Somatic Cell Score by Genetic Parameter Estimation and Genome-Wide Association

Author

Listed:
  • Xubin Lu

    (College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
    College of Veterinary Medicine, Yangzhou University, Yangzhou 225009, China)

  • Hui Jiang

    (College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China)

  • Abdelaziz Adam Idriss Arbab

    (College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China)

  • Bo Wang

    (College of Veterinary Medicine, Yangzhou University, Yangzhou 225009, China)

  • Dingding Liu

    (College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China)

  • Ismail Mohamed Abdalla

    (College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China)

  • Tianle Xu

    (Joint International Research Laboratory of Agriculture and Agri-Product Safety, Yangzhou University, Yangzhou 225009, China)

  • Yujia Sun

    (Joint International Research Laboratory of Agriculture and Agri-Product Safety, Yangzhou University, Yangzhou 225009, China)

  • Zongping Liu

    (College of Veterinary Medicine, Yangzhou University, Yangzhou 225009, China)

  • Zhangping Yang

    (College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China)

Abstract

The quality and safety of milk is challenged by cow mastitis, and the value of somatic cell score (SCS) in milk is closely related to the occurrence of mastitis. This study aimed to analyze the genetic characteristics of SCS across the first three parities in Chinese Holstein cattle, as well as to investigate potential candidate genes and biological processes that may play a potential role in the progress of cow mastitis. In this respect, we evaluated genetic parameters and conducted a genome-wide association study based on the test-day records of SCS for Chinese Holstein cows; we also validated key candidate genes using a quantitative reverse transcription PCR (RT-qPCR) experiment in primary bovine mammary epithelial cells (bMECs). The heritability of the SCS 305-day performance in milk varied between 0.07 and 0.24, and decreased with increasing parity. As the time interval grew larger, the genetic and permanent environmental correlations with the number of days in milk (DIM) weakened. Six significant single-nucleotide polymorphisms (SNPs) were identified in the association analysis, one of which was located within the exonic region of CD44 . This exon-associated SNP may modify the activity of the protein encoded by the CD44 . A total of 32 genes within the two hundred kilobase (kb) range of significant SNPs were detected, and these genes were markedly enriched in eight Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 22 biological processes, mainly participating in the progress of transmembrane transport, inflammatory factor regulation, cellular responses, the Toll-like receptor signaling pathway, and the MAPK signaling pathway. Nine genes, including the PKD2 , KCNAB1 , SLC35A4 , SPP1 , IBSP , CD14 , CD44 , MAPK10 , and ABCG2 genes, were selected as candidate genes that could have critical functions in cow mastitis. These findings can serve as a foundation for molecular breeding and as valuable data for reducing the incidence of mastitis of Chinese Holstein cattle at the molecular level.

Suggested Citation

  • Xubin Lu & Hui Jiang & Abdelaziz Adam Idriss Arbab & Bo Wang & Dingding Liu & Ismail Mohamed Abdalla & Tianle Xu & Yujia Sun & Zongping Liu & Zhangping Yang, 2023. "Investigating Genetic Characteristics of Chinese Holstein Cow’s Milk Somatic Cell Score by Genetic Parameter Estimation and Genome-Wide Association," Agriculture, MDPI, vol. 13(2), pages 1-17, January.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:2:p:267-:d:1043496
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/2/267/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/2/267/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lufen Chang & Michael Karin, 2001. "Mammalian MAP kinase signalling cascades," Nature, Nature, vol. 410(6824), pages 37-40, March.
    2. Xiaolei Liu & Meng Huang & Bin Fan & Edward S Buckler & Zhiwu Zhang, 2016. "Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies," PLOS Genetics, Public Library of Science, vol. 12(2), pages 1-24, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Justin N. Vaughn & Sandra E. Branham & Brian Abernathy & Amanda M. Hulse-Kemp & Adam R. Rivers & Amnon Levi & William P. Wechter, 2022. "Graph-based pangenomics maximizes genotyping density and reveals structural impacts on fungal resistance in melon," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. Kazunari Iwamoto & Yuki Shindo & Koichi Takahashi, 2016. "Modeling Cellular Noise Underlying Heterogeneous Cell Responses in the Epidermal Growth Factor Signaling Pathway," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-18, November.
    3. Hai Anh Tran & Hyun Jo & Thi Cuc Nguyen & Jeong-Dong Lee & Hak Soo Seo & Jong Tae Song, 2024. "Genome-Wide Association Analysis for Submergence Tolerance at the Early Vegetative and Germination Stages in Wild Soybean ( Glycine soja )," Agriculture, MDPI, vol. 14(9), pages 1-17, September.
    4. Zhanwei Zhuang & Shaoyun Li & Rongrong Ding & Ming Yang & Enqin Zheng & Huaqiang Yang & Ting Gu & Zheng Xu & Gengyuan Cai & Zhenfang Wu & Jie Yang, 2019. "Meta-analysis of genome-wide association studies for loin muscle area and loin muscle depth in two Duroc pig populations," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-21, June.
    5. Guangbao Guo & Guoqi Qian & Lu Lin & Wei Shao, 2021. "Parallel inference for big data with the group Bayesian method," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(2), pages 225-243, February.
    6. Gianola, Daniel & Fernando, Rohan L. & Schön, Chris-Carolin, 2020. "Inferring trait-specific similarity among individuals from molecular markers and phenotypes with Bayesian regression," Theoretical Population Biology, Elsevier, vol. 132(C), pages 47-59.
    7. Thomas C Whisenant & David T Ho & Ryan W Benz & Jeffrey S Rogers & Robyn M Kaake & Elizabeth A Gordon & Lan Huang & Pierre Baldi & Lee Bardwell, 2010. "Computational Prediction and Experimental Verification of New MAP Kinase Docking Sites and Substrates Including Gli Transcription Factors," PLOS Computational Biology, Public Library of Science, vol. 6(8), pages 1-21, August.
    8. Chih-Chien Wang & Chih-Yun Huang & Meng-Chang Lee & Dung-Jang Tsai & Chia-Chun Wu & Sui-Lung Su, 2021. "Genetic association between TNF-α G-308A and osteoarthritis in Asians: A case–control study and meta-analysis," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-15, November.
    9. Luca Marchetti & Rosario Lombardo & Corrado Priami, 2017. "HSimulator: Hybrid Stochastic/Deterministic Simulation of Biochemical Reaction Networks," Complexity, Hindawi, vol. 2017, pages 1-12, December.
    10. Xiaojun Mao & Somak Dutta & Raymond K. W. Wong & Dan Nettleton, 2020. "Adjusting for Spatial Effects in Genomic Prediction," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(4), pages 699-718, December.
    11. Satoya Yoshida & Tatsuya Yoshida & Kohei Inukai & Katsuhiro Kato & Yoshimitsu Yura & Tomoki Hattori & Atsushi Enomoto & Koji Ohashi & Takahiro Okumura & Noriyuki Ouchi & Haruya Kawase & Nina Wettschur, 2024. "Protein kinase N promotes cardiac fibrosis in heart failure by fibroblast-to-myofibroblast conversion," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    12. Jason W Locasale & Arup K Chakraborty, 2008. "Regulation of Signal Duration and the Statistical Dynamics of Kinase Activation by Scaffold Proteins," PLOS Computational Biology, Public Library of Science, vol. 4(6), pages 1-12, June.
    13. Ganwen Zhang & Jianini Zhao & Jieru Wang & Guo Lin & Lin Li & Fengfei Ban & Meiting Zhu & Yangjun Wen & Jin Zhang, 2024. "An Improved Expectation–Maximization Bayesian Algorithm for GWAS," Mathematics, MDPI, vol. 12(13), pages 1-14, June.
    14. Lina Chen & Wan Li & Liangcai Zhang & Hong Wang & Weiming He & Jingxie Tai & Xu Li & Xia Li, 2011. "Disease Gene Interaction Pathways: A Potential Framework for How Disease Genes Associate by Disease-Risk Modules," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-12, September.
    15. Peter Rashkov & Ian P Barrett & Robert E Beardmore & Claus Bendtsen & Ivana Gudelj, 2016. "Kinase Inhibition Leads to Hormesis in a Dual Phosphorylation-Dephosphorylation Cycle," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-15, November.
    16. Prabin Bajgain & James A. Anderson, 2021. "Multi-Allelic Haplotype-Based Association Analysis Identifies Genomic Regions Controlling Domestication Traits in Intermediate Wheatgrass," Agriculture, MDPI, vol. 11(7), pages 1-15, July.
    17. Raffaele Pezzilli & Antonio M. Morselli-Labate, 2009. "Alcoholic Pancreatitis: Pathogenesis, Incidence and Treatment with Special Reference to the Associated Pain," IJERPH, MDPI, vol. 6(11), pages 1-20, November.
    18. Yue Xin & Lina Gao & Wenming Hu & Qi Gao & Bin Yang & Jianguo Zhou & Cuilian Xu, 2022. "Genome-Wide Association Study Based on Plant Height and Drought-Tolerance Indices Reveals Two Candidate Drought-Tolerance Genes in Sweet Sorghum," Sustainability, MDPI, vol. 14(21), pages 1-14, November.
    19. Zhaoxuan Che & Jiakun Qiao & Fangjun Xu & Xinyun Li & Yunxia Zhao & Mengjin Zhu, 2024. "Integrated Analysis Reveals Genetic Basis of Growth Curve Parameters in an F 2 Designed Pig Population Based on Genome and Transcriptome Data," Agriculture, MDPI, vol. 14(10), pages 1-18, September.
    20. Uğur Sesiz, 2023. "Deciphering Genomic Regions and Putative Candidate Genes for Grain Size and Shape Traits in Durum Wheat through GWAS," Agriculture, MDPI, vol. 13(10), pages 1-17, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:13:y:2023:i:2:p:267-:d:1043496. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.