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Deciphering genomic signatures in different Indian milch cattle breeds
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In the present study, the population genomic data of different cattle breeds were explored to decipher the genomic regions affected due to selective events and reflected in the productive, reproductive, thermo-tolerance, and health-related traits. To find out these genomic deviations due to selective sweeps, four statistical tools (Tajima’s D, CLR, ROH, and iHS) were used on four indigenous cattle breeds. Several candidate genes were found to be related to milk production (ADARB, WDR70, and CA8), reproductive (PARN, FAM134B2, and ZBTB20), and health-related traits (SP110, CXCL2, CLXCL3, CXCL5, IRF8, and MYOM1). Our findings serve as a foundation for identifying selective sweeps responsible for the genetic variation of traits. These sweeps could hold functional significance for multiple cattle breeds across various subcontinents. However, to enhance the robustness of the results, further studies with high-density arrays or whole-genome sequencing, employing greater resolution and larger sample sizes, are warranted.
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Title: Deciphering genomic signatures in different Indian milch cattle breeds
Description:
In the present study, the population genomic data of different cattle breeds were explored to decipher the genomic regions affected due to selective events and reflected in the productive, reproductive, thermo-tolerance, and health-related traits.
To find out these genomic deviations due to selective sweeps, four statistical tools (Tajima’s D, CLR, ROH, and iHS) were used on four indigenous cattle breeds.
Several candidate genes were found to be related to milk production (ADARB, WDR70, and CA8), reproductive (PARN, FAM134B2, and ZBTB20), and health-related traits (SP110, CXCL2, CLXCL3, CXCL5, IRF8, and MYOM1).
Our findings serve as a foundation for identifying selective sweeps responsible for the genetic variation of traits.
These sweeps could hold functional significance for multiple cattle breeds across various subcontinents.
However, to enhance the robustness of the results, further studies with high-density arrays or whole-genome sequencing, employing greater resolution and larger sample sizes, are warranted.
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