Genomic scan for selection signatures associated with mastitis in German Holstein cattle

Document Type : Research Paper


1 Ph.D. Candidate, Department of Animal Science and Fisheries, Sari Agricultural Science and Natural Resources University, Sari, Iran

2 Professor, Department of Animal Science and Fisheries, Sari Agricultural Science and Natural Resources University, Sari, Iran

3 Associate Professor, Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

4 Assistant Professor, Department of Animal Science, Arak University, Arak, Iran

5 Professor, Department of Animal Science, Kassel University, Witzenhausen, Germany


Domestication and selection has changed behavioral and phenotypic characteristics in modern domestic animals significantly. The selection of animals by humans left detectable signatures on the genome of modern cattle. The identification of these signals can help us to improve the genetic characteristics of economically important traits in cattle. Nowadays, mastitis is one of the main economically important diseases in dairy cattle that mostly caused by intense selection for milk production in recent decades. In this study the genomic regions associated with mastitis, the genomic data of national project in Germany Holstein dairy cattle was used to identify. The samples were genotyped using Illumina Bovine 50K SNP. 133 case and 133 control cows were chosen for investigating of selection signatures using Theta (θ) population differentiation statistics. With 99.90 percentile threshold of the obtained Theta (θ) values, 10 genomic regions on chromosomes 1 (2 regions), 3, 5, 6, 14 (2 regions), 21 (2 regions) and 28 were identified. Further investigation using bioinformatics tools showed these genomic regions overlapped with the genes associated with immune system, autoimmune diseases, different type of cancers expressly breast cancer and milk production. In conclusion, the results of this study may provide an important source to facilitate the identification of genomic regions and then, the genes affecting mastitis in dairy cows.


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