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Composite selection signal analysis: Uncovering candidate genes and quantitative trait loci in Indian sheep breeds

Author

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  • Sapna Nath
  • Satish Kumar Illa
  • Destaw Worku
  • Sabyasachi Mukherjee
  • Anupama Mukherjee
  • Vinod Kumar Yata

Abstract

Selective pressures, both natural and artificial, have significantly influenced the genomic architecture of domesticated sheep. Understanding their underlying molecular mechanisms is critical for developing efficient breeding programs to conserve and improve economically important traits in native breeds. In this study, we analysed high-density 50K SNP data from three Indigenous sheep breeds: Chanthangi (CHA, n = 29), Garole (GAR, n = 24), and Deccani (IDC, n = 26), each native to diverse climatic regions of India. We implemented a novel SNP-based de-correlated composite of multiple signals (DCMS) statistic, which integrates p-values from five selection metrics viz., FST, H1, H12, Tajima’s D, and nucleotide diversity (π) into a unified measure. The SNP-based DCMS approach offers finer resolution and complements window-based methods by enabling more precise localisation of selection signals and candidate genes. Multiple testing correction was applied at a False Discovery Rate (FDR) threshold of

Suggested Citation

  • Sapna Nath & Satish Kumar Illa & Destaw Worku & Sabyasachi Mukherjee & Anupama Mukherjee & Vinod Kumar Yata, 2026. "Composite selection signal analysis: Uncovering candidate genes and quantitative trait loci in Indian sheep breeds," PLOS ONE, Public Library of Science, vol. 21(3), pages 1-21, March.
    • Handle: RePEc:plo:pone00:0344299
    DOI: 10.1371/journal.pone.0344299
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