Meta-analysis of six dairy cattle breeds reveals biologically relevant candidate genes for mastitis resistance
2024
Cai, Zexi | Iso-Touru, Terhi | Sanchez, Marie-Pierre | Kadri, Naveen | Bouwman, Aniek, C | Chitneedi, Praveen Krishna | Macleod, Iona, M | Vander Jagt, Christy, J | Chamberlain, Amanda, J | Gredler-Grandl, Birgit | Spengeler, Mirjam | Lund, Mogens Sandø | Boichard, Didier | Kühn, Christa | Pausch, Hubert | Vilkki, Johanna | Sahana, Goutam | Aarhus University [Aarhus] | Center for Quantitative Genetics and Genomics ; Aarhus University [Aarhus] | Natural Resources Institute Finland (LUKE) | Génétique Animale et Biologie Intégrative (GABI) ; AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich) | Wageningen University and Research [Wageningen] (WUR) | Leibniz Institute for Farm Animal Biology (FBN) | Agriculture Victoria (AgriBio) | QualitasAG, 6300 Zug, Switzerland | University of Rostock = Universität Rostock | European Project: 815668,H2020-SFS-2018-2020,H2020-SFS-2018-2,BovReg(2019)
International audience
显示更多 [+] 显示较少 [-]英语. Mastitis is a disease that incurs significant costs in the dairy industry. A promising approach to mitigate its negative effects is to genetically improve the resistance of dairy cattle to mastitis. A meta-analysis of genome-wide association studies (GWAS) across multiple breeds for clinical mastitis (CM) and its indicator trait, somatic cell score (SCS), is a powerful method to identify functional genetic variants that impact mastitis resistance. We conducted meta-analyses of eight and fourteen GWAS on CM and SCS, respectively, using 30,689 and 119,438 animals from six dairy cattle breeds. Methods for the meta-analyses were selected to properly account for the multi-breed structure of the GWAS data. Our study revealed 58 lead markers that were associated with mastitis incidence, including 16 loci that did not overlap with previously identified quantitative trait loci (QTL), as curated at the Animal QTLdb. Post-GWAS analysis techniques such as gene-based analysis and genomic feature enrichment analysis enabled prioritization of 31 candidate genes and 14 credible candidate causal variants that affect mastitis. Our list of candidate genes can help to elucidate the genetic architecture underlying mastitis resistance and provide better tools for the prevention or treatment of mastitis, ultimately contributing to more sustainable animal production.
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