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Vol. 12, Issue 5 (2023)

Comprehensive review of selection signature identification: Exploring frequency-based (FST) and Haplotype-Based (iHS) methods

Author(s):
Jigyasha, Smriti Sharma, Lalmuansangi, Ravina, Chandana Sree Chinnareddyvari, Akshata Patil, Sahana VN, Shivam Bhardwaj, Kashif Dawood Khan, Anil, Anju Rohilla and Pallavi Rathi
Abstract:
Genome-wide detection of selection signatures in livestock populations" makes use of high-throughput technologies to spot genetic alterations brought on by selective breeding. This knowledge can be utilized to identify potential genes that are being selected for traits that are connected with economic importance, as well as to comprehend the breeding and process of evolution that have shaped livestock populations. Identifying selection signatures is a potent approach for comprehending the underlying genetics of livestock traits and strengthening livestock breeding programs. By targeting genes under selection, breeders can improve the performance and productivity of livestock populations, which can lead to increased profits and better food security. Positive selection can fix favourable allele frequencies throughout a population and raise their frequencies, which reduces diversity at or near the selection site (Selection Sweep). Variations in allele frequency (FST) and the deterioration of linkage disequilibrium (iHS) can be used to identify selection signatures. The goals of this review aim to give us a better understanding of how to identify selection signals in livestock, which will inform us about the genetic mechanism behind phenotypic variations within and between breeds.
Pages: 3803-3808  |  376 Views  244 Downloads


The Pharma Innovation Journal
How to cite this article:
Jigyasha, Smriti Sharma, Lalmuansangi, Ravina, Chandana Sree Chinnareddyvari, Akshata Patil, Sahana VN, Shivam Bhardwaj, Kashif Dawood Khan, Anil, Anju Rohilla, Pallavi Rathi. Comprehensive review of selection signature identification: Exploring frequency-based (FST) and Haplotype-Based (iHS) methods. Pharma Innovation 2023;12(5):3803-3808.

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