IMMUNOGENETICS OF DISEASE RESISTANCE IN LIVESTOCK

Authors

  • Aftab Ahmed Livestock & Dairy Development (Extension) Department, Khyber Pakhtunkhwa, Pakistan Author

DOI:

https://doi.org/10.66406/gjls0132

Keywords:

Immunogenetics, Livestock Disease Resistance, Mixed-Methods Research, Machine Learning, Phenotypic Assessment, Snp Analysis

Abstract

 Immunogenetic foundation of disease resistance in cattle breeds is considered in the study and experimental design of the study is mixed method complex which involves genetic data, phenotypic data as well as ethnoveterinary data.  PCR amplification and next-generation sequencing was used to search specifically for immune-related genetic variability in blood and tissue samples of multiple breeds.  Phenotypic resistance was tested by performing standardised clinical scoring in a controlled environment using pathogens. Traditional knowledge on resilience of breeds in the area was also collected using organized interviews with local herders.  Quantitative examination depicted that some of the single nucleotide polymorphisms particularly in the major histocompatibility complex area were highly associated with increased scores of resistance.  Techniques of machine learning, such as gradient boosting and random forest classifiers, were highly predictive (AUC > 0.90), and SHAP analysis revealed relevant novel genetic traits that influence resistance.  The historically resilient breeds were proved to be better in phenotypic studies and the qualitative results aided in elucidating the perceived genetic patterns that were observed.  The pairing of molecular genetics with statistical modelling, as well as local knowledge, provides us with a comprehensive answer to locating and propagating genetically flexible breeds.  The findings directly impacts on selective breeding projects, sustainable livestock production and design of precision agrarian apparatus to enhance health, production and yield of herds.

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Published

2024-06-30

How to Cite

IMMUNOGENETICS OF DISEASE RESISTANCE IN LIVESTOCK. (2024). Gomal Journal of Life Sciences, 2(01), 22-41. https://doi.org/10.66406/gjls0132