Dissecting loci that underpin the genetic correlations between production, fertility, and urea traits in Australian Holstein cattle

Author:

Olasege Babatunde S.12ORCID,van den Berg Irene3,Haile‐Mariam Mekonnen34,Ho Phuong N.3,Yin Oh Zhen1,Porto‐Neto Laercio R.2,Hayes Ben J.5,Pryce Jennie E.34,Fortes Marina R. S.15ORCID

Affiliation:

1. School of Chemistry and Molecular Biosciences, The University of Queensland Brisbane Queensland Australia

2. CSIRO Agriculture and Food Saint Lucia Queensland Australia

3. Agriculture Victoria, AgriBio, Centre for AgriBioscience Bundoora Victoria Australia

4. School of Applied Systems Biology, La Trobe University Bundoora Victoria Australia

5. The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI) Brisbane Queensland Australia

Abstract

AbstractUnfavorable genetic correlations between milk production, fertility, and urea traits have been reported. However, knowledge of the genomic regions associated with these unfavorable correlations is limited. Here, we used the correlation scan method to identify and investigate the regions driving or antagonizing the genetic correlations between production vs. fertility, urea vs. fertility, and urea vs. production traits. Driving regions produce an estimate of correlation that is in the same direction as the global correlation. Antagonizing regions produce an estimate in the opposite direction of the global estimates. Our dataset comprised 6567, 4700, and 12,658 Holstein cattle with records of production traits (milk yield, fat yield, and protein yield), fertility (calving interval) and urea traits (milk urea nitrogen and blood urea nitrogen predicted using milk‐mid‐infrared spectroscopy), respectively. Several regions across the genome drive the correlations between production, fertility, and urea traits. Antagonizing regions were confined to certain parts of the genome and the genes within these regions were mostly involved in preventing metabolic dysregulation, liver reprogramming, metabolism remodeling, and lipid homeostasis. The driving regions were enriched for QTL related to puberty, milk, and health‐related traits. Antagonizing regions were mostly related to muscle development, metabolic body weight, and milk traits. In conclusion, we have identified genomic regions of potential importance for dairy cattle breeding. Future studies could investigate the antagonizing regions as potential genomic regions to break the unfavorable correlations and improve milk production as well as fertility and urea traits.

Publisher

Wiley

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