| Autor: |
Zexi Cai, Terhi Iso-Touru, Marie-Pierre Sanchez, Naveen Kadri, Aniek C. Bouwman, Praveen Krishna Chitneedi, Iona M. MacLeod, Christy J. Vander Jagt, Amanda J. Chamberlain, Birgit Gredler-Grandl, Mirjam Spengeler, Mogens Sandø Lund, Didier Boichard, Christa Kühn, Hubert Pausch, Johanna Vilkki, Goutam Sahana |
| Jazyk: |
German<br />English<br />French |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Genetics Selection Evolution, Vol 56, Iss 1, Pp 1-22 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1297-9686 |
| DOI: |
10.1186/s12711-024-00920-8 |
| Popis: |
Abstract Background 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. Results 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. Conclusions 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
Full text from SpringerLink