Mapping genomic regions affecting sensitivity to bovine respiratory disease on chromosome X using selective DNA pooling

Ehud Lipkin, Maria Giuseppina Strillacci, Miri Cohen-Zinder, Harel Eitam, Moran Yishay, Morris Soller, Carlotta Ferrari, Alessandro Bagnato, Ariel Shabtay

Research output: Contribution to journalArticlepeer-review

Abstract

Bovine respiratory disease is a leading health problem in feedlot cattle. Identification of affecting genes is essential for selection for decrease sensitivity. Chromosome X is a special attractive target for gene mapping in light of reports on both sexual dimorphism in immunity and higher susceptibility of males to this disease. However, diagnosis is challenging and clinical signs often go undetected. Kosher scoring of lung adhesions was used as a cost-effective proxy diagnosis. Selective DNA pooling was applied for cost-effective mapping of regions associated with sensitivity to the disease on chromosome X in Israeli Holstein male calves. A total of 9 regions were found, more than twice of any of the autosomes. All regions overlapped or were very close to previously reported regions. Bioinformatics survey found candidate-by-location genes in these regions. Functional analyses identified candidates-by-function among these genes. Network analyses connected the genes and found possible relations of the genes and the networks with morbidity, and specifically with sensitivity to bovine respiratory disease. The relatively large number of affecting regions and the candidate genes on the sex chromosome may explain part of the higher susceptibility of males and provide genomic and management targets for mitigating this disease.

Original languageEnglish
Pages (from-to)4556
Number of pages1
JournalScientific Reports
Volume15
Issue number1
DOIs
StatePublished - 7 Feb 2025
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2025. The Author(s).

Keywords

  • Bovine respiratory disease (BRD)
  • Candidate genes
  • Immunity sexual dimorphism
  • Kosher scoring
  • Quantitative trait regions (QTLRs)

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