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<p>India harbours a diverse range of indigenous goat breeds that have adapted to varied climatic zones over centuries. This study investigated the genomic basis of local adaptation in these populations (n= 11) divided into seven agro-climatic zones using genome-wide SNP data and century-scale environmental variables. A total of 2,295,833 SNPs and 15 non-collinear bioclimatic predictors were analyzed using the landscape genomics tool R SamBada for genotype–environment association. Models were selected based on G-score and q-value thresholds (q &lt; 0.01). Several loci showed strong signatures of selection, with associated genes enriched in key adaptive pathways, including HIF-1 signalling, insulin signalling, and toll-like receptor pathways. Many key genes and pathways were identified with both direct and indirect roles in adaptation to specific agro-climatic zone. Only 9 SNP variants showed SIFT score &lt; 0.05 (deleterious) out of which, only 2 variants each harbouring gene PTPRC and PLCB1 were predicted to be deleterious with high confidence. Further downstream technical validation for functionality was done using PTPRC and PLCB1 present in coding region and exhibited significant environmental associations. Missense mutations in these genes were further characterized using I-Mutant, ConSurf, and Phyre2. The PTPRC variant was predicted to reduce protein stability within a moderately conserved immune domain, and structural modelling indicated altered folding in mutant proteins. These adaptive variants likely contribute to resilience against heat, humidity, and pathogen-driven stress. This integrative landscape genomics approach reveals how natural selection and environmental pressures have shaped the adaptive genome of Indian indigenous goats and provides a foundation for marker-assisted selection to enhance climate resilience in future breeding programs. This study represents the first landscape genomics analysis in indigenous goat populations of India. </p>

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Mapping genomic adaptation to environmental heterogeneity in Indian native goat populations through landscape genomics

  • 期刊论文

  • 发表日期：

    2026年4月6日

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  • Pallavi Rathi,
  • Nidhi Sukhija,
  • Indrajit Ganguly,
  • S. P. Dixit,
  • Sanjeev Singh,
  • Chandana Sree Chinnareddyvari,
  • C. A. Dharaamshaw

摘要

India harbours a diverse range of indigenous goat breeds that have adapted to varied climatic zones over centuries. This study investigated the genomic basis of local adaptation in these populations (n= 11) divided into seven agro-climatic zones using genome-wide SNP data and century-scale environmental variables. A total of 2,295,833 SNPs and 15 non-collinear bioclimatic predictors were analyzed using the landscape genomics tool R SamBada for genotype–environment association. Models were selected based on G-score and q-value thresholds (q < 0.01). Several loci showed strong signatures of selection, with associated genes enriched in key adaptive pathways, including HIF-1 signalling, insulin signalling, and toll-like receptor pathways. Many key genes and pathways were identified with both direct and indirect roles in adaptation to specific agro-climatic zone. Only 9 SNP variants showed SIFT score < 0.05 (deleterious) out of which, only 2 variants each harbouring gene PTPRC and PLCB1 were predicted to be deleterious with high confidence. Further downstream technical validation for functionality was done using PTPRC and PLCB1 present in coding region and exhibited significant environmental associations. Missense mutations in these genes were further characterized using I-Mutant, ConSurf, and Phyre2. The PTPRC variant was predicted to reduce protein stability within a moderately conserved immune domain, and structural modelling indicated altered folding in mutant proteins. These adaptive variants likely contribute to resilience against heat, humidity, and pathogen-driven stress. This integrative landscape genomics approach reveals how natural selection and environmental pressures have shaped the adaptive genome of Indian indigenous goats and provides a foundation for marker-assisted selection to enhance climate resilience in future breeding programs. This study represents the first landscape genomics analysis in indigenous goat populations of India.