Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus, Denmark.
Natural Resources Institute Finland (Luke), 31600, Jokioinen, Finland.
Genet Sel Evol. 2024 Jul 15;56(1):54. doi: 10.1186/s12711-024-00920-8.
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.
乳腺炎是奶牛养殖业中一种代价高昂的疾病。一种有前途的减轻其负面影响的方法是通过基因改良来提高奶牛对乳腺炎的抗性。对多个品种的临床乳腺炎（CM）和其指标性状体细胞评分（SCS）的全基因组关联研究（GWAS）进行荟萃分析，是一种识别影响乳腺炎抗性的功能遗传变异的有力方法。
我们使用来自六个奶牛品种的 30689 头和 119438 头动物，分别对 CM 和 SCS 进行了 8 项和 14 项 GWAS 的荟萃分析。荟萃分析的方法选择适当考虑了 GWAS 数据的多品种结构。我们的研究揭示了 58 个与乳腺炎发生率相关的主要标记物，包括 16 个与之前在动物 QTLdb 中鉴定的数量性状基因座（QTL）不重叠的位点。GWAS 后分析技术，如基于基因的分析和基因组特征富集分析，有助于确定 31 个候选基因和 14 个可信的候选因果变异，这些变异会影响乳腺炎。
我们的候选基因列表可以帮助阐明乳腺炎抗性的遗传结构，并为乳腺炎的预防或治疗提供更好的工具，最终有助于更可持续的动物生产。
