[en] The aim of this study was to detect the genomic region or regions associated with metabolic clusters in early-lactation Holstein cows. This study was carried out in 2 experiments. In experiment I, which was carried out on 105 multiparous Holstein cows, animals were classified through k-means clustering on log-transformed and standardized concentrations of blood glucose, insulin-like growth factor I, free fatty acids, and β-hydroxybutyrate at 14 and 35 d in milk (DIM), into metabolic clusters, either balanced (BAL) or other (OTR). Forty percent of the animals were categorized in the BAL group, and the remainder were categorized as OTR. The cows were genotyped for a total of 777,962 SNP. A genome-wide association study was performed, using a case-control approach through the GEMMA software, accounting for population structure. We found 8 SNP (BTA11, BTA23, and BTAX) associated with the predicted metabolic clusters. In experiment II, carried out on 4,267 second-parity Holstein cows, milk samples collected starting from the first week until 50 DIM were used to determine Fourier-transform mid-infrared (FT-MIR) spectra and subsequently to classify the animals into the same metabolic clusters (BAL vs. OTR). Twenty-eight percent of the animals were categorized in the BAL group, and the remainder were classified in the OTR category. Although daily milk yield was lower in BAL cows, we found no difference in daily fat- and protein-corrected milk yield in cows from the BAL metabolic cluster compared with those in the OTR metabolic cluster. In the next step, a single-step genomic BLUP was used to identify the genomic region(s) associated with the predicted metabolic clusters. The results revealed that prediction of metabolic clusters is a highly polygenic trait regulated by many small-sized effects. The region of 36,258 to 36,295 kb on BTA27 was the highly associated region for the predicted metabolic clusters, with the closest genes to this region (ANK1 and miR-486) being related to hematopoiesis, erythropoiesis, and mammary gland development. The heritability for metabolic clustering was 0.17 (SD 0.03), indicating that the use of FT-MIR spectra in milk to predict metabolic clusters in early-lactation across a large number of cows has satisfactory potential to be included in genetic selection programs for modern dairy cows.
Disciplines :
Animal production & animal husbandry
Author, co-author :
Atashi, Hadi ; Université de Liège - ULiège > Département GxABT > Animal Sciences (AS) ; Department of Reproduction, Obstetrics and Herd Health, Ghent University, Merelbeke 9820, Belgium, Department of Animal Science, Shiraz University, Shiraz 71441-65186, Iran
Salavati, M; The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian EH25 9RG, UK
De Koster, J; Department of Reproduction, Obstetrics and Herd Health, Ghent University, Merelbeke 9820, Belgium
Crowe, M A; University College Dublin, 4 Dublin, Ireland
Opsomer, G; Department of Reproduction, Obstetrics and Herd Health, Ghent University, Merelbeke 9820, Belgium
GplusE consortium
Hostens, M; Department of Reproduction, Obstetrics and Herd Health, Ghent University, Merelbeke 9820, Belgium. Electronic address: miel.hostens@ugent.be
Language :
English
Title :
Genome-wide association for metabolic clusters in early-lactation Holstein dairy cows.
This project received funding from the European Union's Seventh Framework Program (Brussels, Belgium) for research, technological development, and demonstration, under grant agreement no. 613689. The views expressed in this publication are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission. Authors within the GplusE consortium: Alan Fahey, Elizabeth Matthews, Andreia Santoro, Colin Byrne, Pauline Rudd, Roisin O'Flaherty, Sinead Hallinan, Claire Wathes, Zhangrui Cheng, Ali Fouladi, Geoff Pollott, Dirk Werling, Beatriz Sanz Bernardo, Alistair Wylie, Matt Bell, Mieke Vaneetvelde, Kristof Hermans, Geert Opsomer, Sander Moerman, Jenne De Koster, Hannes Bogaert, Jan Vandepitte, Leila Vandevelde, Bonny Vanranst, Johanna Hoglund, Susanne Dahl, Soren Ostergaard, Janne Rothmann, Mogens Krogh, Else Meyer, Charlotte Gaillard, Jehan Ettema, Tine Rousing, Federica Signorelli, Francesco Napolitano, Bianca Moioli, Alessandra Crisà, Luca Buttazzoni, Jennifer McClure, Daragh Matthews, Francis Kearney, Andrew Cromie, Matt McClure, Shujun Zhang, Xing Chen, Huanchun Chen, Junlong Zhao, Liguo Yang, Guohua Hua, Chen Tan, Guiqiang Wang, Michel Bonneau, Andrea Pompozzi, Armin Pearn, Arnold Evertson, Linda Kosten, Anders Fogh, Thomas Andersen, Matthew Lucey, Chris Elsik, Gavin Conant, Jerry Taylor, Nicolas Gengler, Michel Georges, Frédéric Colinet, Hedi Hammami, Catherine Bastin, Haruko Takeda, Aurelie Laine, Anne-Sophie Van Laere, Martin Schulze, Sergio Palma Vera, Conrad Ferris, Cinzia Marchitelli. The authors declare no potential conflict of interest associated with this research.
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