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Deciphering climate resilience in Indian cattle breeds by selection signature analyses

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Abstract

The signature of selection is a crucial concept in evolutionary biology that refers to the pattern of genetic variation which arises in a population due to natural selection. In the context of climate adaptation, the signature of selection can reveal the genetic basis of adaptive traits that enable organisms to survive and thrive in changing environmental conditions. Breeds living in diverse agroecological zones exhibit genetic “footprints” within their genomes that mirror the influence of climate-induced selective pressures, subsequently impacting phenotypic variance. It is assumed that the genomes of animals residing in these regions have been altered through selection for various climatic adaptations. These regions are known as signatures of selection and can be identified using various summary statistics. We examined genotypic data from eight different cattle breeds (Gir, Hariana, Kankrej, Nelore, Ongole, Red Sindhi, Sahiwal, and Tharparkar) that are adapted to diverse regional climates. To identify selection signature regions in this investigation, we used four intra-population statistics: Tajima’s D, CLR, iHS, and ROH. In this study, we utilized Bovine 50 K chip data and four genome scan techniques to assess the genetic regions of positive selection for high-temperature adaptation. We have also performed a genome-wide investigation of genetic diversity, inbreeding, and effective population size in our target dataset. We identified potential regions for selection that are likely to be caused by adverse climatic conditions. We observed many adaptation genes in several potential selection signature areas. These include genes like HSPB2, HSPB3, HSP20, HSP90AB1, HSF4, HSPA1B, CLPB, GAP43, MITF, and MCHR1 which have been reported in the cattle populations that live in varied climatic regions. The findings demonstrated that genes involved in disease resistance and thermotolerance were subjected to intense selection. The findings have implications for marker-assisted breeding, understanding the genetic landscape of climate-induced adaptation, putting breeding and conservation programs into action.

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Data availability

The WIDDE database and the Dryad repository both contain the genotypic data used for this study (http://widde.toulouse.inra.fr/widde/). The genotypic information for the 72 Tharparkar animals utilized in this work is accessible with the corresponding author, and due to some institutional liabilities, we can disclose this dataset on request.

References

  • Abioja, M. O., Logunleko, M. O., Majekodunmi, B. C., Adekunle, E. O., Shittu, O. O., Odeyemi, A. J., ... & Daramola, J. O. (2022). Roles of Candidate Genes in the Adaptation of Goats to Heat Stress: A Review. Small Ruminant Research, 106878.

  • Ahmad, S.F., Panigrahi, M., Chhotaray, S., Pal, D., Parida, S., Bhushan, B., Gaur, G.K., Mishra, B.P. and Singh, R.K. (2020). Revelation of genomic breed composition in a crossbred cattle of India with the help of Bovine50K BeadChip. Genomics, 112(2), 1531-1535.

    Article  CAS  PubMed  Google Scholar 

  • Ajmone-Marsan, P. and Globaldiv Consortium, 2010. A global view of livestock biodiversity and conservation–GLOBALDIV. Animal genetics, 41, pp.1-5.

    Article  Google Scholar 

  • Al-Massadi, O., Dieguez, C., Schneeberger, M. et al. Multifaceted actions of melanin-concentrating hormone on mammalian energy homeostasis. Nat Rev Endocrinol 17, 745–755 (2021).

    Article  CAS  PubMed  Google Scholar 

  • Amaral AJ, Ferretti L, Megens HJ, Crooijmans RPMA, Nie HS, RamosOnsins SE, Perez-Enciso M, Schook LB, Groenen MAM. 2011. Genome-wide footprints of pig domestication and selection revealed through massive parallel sequencing of pooled DNA. PLoS One 6:e14782.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Almeida, O.A.C., Moreira, G.C.M., Rezende, F.M., Boschiero, C., de Oliveira Peixoto, J., Ibelli, A.M.G., Ledur, M.C., de Novais, F.J. and Coutinho, L.L., (2019). Identification of selection signatures involved in performance traits in a paternal broiler line. BMC genomics, 20(1), 1-20.

    Article  Google Scholar 

  • Bamshad, M., & Wooding, S. P. (2003). Signatures of natural selection in the human genome. Nature Reviews Genetics, 4(2), 99-110.

    Article  CAS  PubMed  Google Scholar 

  • Barbato M, Orozco-terWengel P, Tapio M, Bruford MW. SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data. Front Genet. 2015;6:109.

    Article  PubMed  PubMed Central  Google Scholar 

  • Bharati, J., Dangi, S. S., Mishra, S. R., Chouhan, V. S., Verma, V., Shankar, O., ... & Sarkar, M. (2017). Expression analysis of toll like receptors and interleukins in Tharparkar cattle during acclimation to heat stress exposure. Journal of Thermal Biology, 65, 48–56.

  • Choudhary, R., Goud, T. S., Kumar, A., Sharma, A. K., Singh, S. V., Upadhyay, R. C., ... & Kumar, S. (2017). Heat stress induced adaptation in melanocytes is dependent on the level of melanin and reduction of apoptosis. Journal of Dermatological Science, 85(3), 250–252.

  • Chhotaray, S., Panigrahi, M., Pal, D., Ahmad, S.F., Bhushan, B., Gaur, G.K., Mishra, B.P. and Singh, R.K., 2020. Ancestry informative markers derived from discriminant analysis of principal components provide important insights into the composition of crossbred cattle. Genomics, 112(2), 1726-1733.

    Article  CAS  PubMed  Google Scholar 

  • Chhotaray, S., Panigrahi, M., Bhushan, B., Gaur, G.K., Dutt, T., Mishra, B.P. and Singh, R.K. (2021). Genome-wide association study reveals genes crucial for coat color production in Vrindavani cattle. Livestock Science, 247, 104476.

    Article  Google Scholar 

  • Danecek, P., Auton, A., Abecasis, G., Albers, C. A., Banks, E., DePristo, M. A., ... & 1000 Genomes Project Analysis Group. (2011). The variant call format and VCFtools. Bioinformatics, 27(15), 2156-2158.

  • Deloulme, J. C., Janet, T., Au, D., Storm, D. R., Sensenbrenner, M., & Baudier, J. (1990). Neuromodulin (GAP43): a neuronal protein kinase C substrate is also present in 0–2A glial cell lineage. Characterization of neuromodulin in secondary cultures of oligodendrocytes and comparison with the neuronal antigen. The Journal of cell biology, 111(4), 1559–1569.

  • Do C, Waples RS, Peel D, et al. NeEstimator v2: reimplementation of software for the estimation of contemporary effective population size (Ne) from genetic data . Mol Ecol Resour. 2014;14(1):209–214. 37.

  • Edea, Z., Dadi, H., Dessie, T., Kim, K.S. (2019). Genomic Signatures of High-Altitude Adaptation in Ethiopian Sheep Popula-Tions. Genes Genom. 41, 973–981.

    Article  Google Scholar 

  • Fariello MI, Boitard S, Naya H, SanCristobal M, Servin B. 2013. Detecting signatures of selection through haplotype differentiation among hierarchically structured populations. Genetics 193: 929–941

    Article  PubMed  PubMed Central  Google Scholar 

  • Friedrich, J. and Wiener, P., 2020. Selection signatures for high-altitude adaptation in ruminants. Animal Genetics, 51(2), 157-165.

    Article  CAS  PubMed  Google Scholar 

  • Gao, Y., Gautier, M., Ding, X., Zhang, H., Wang, Y., Wang, X., ... & Zhang, Y. (2017). Species composition and environmental adaptation of indigenous Chinese cattle. Scientific reports, 7(1), 1–14.

  • Gautier, M., & Vitalis, R. (2012). rehh: an R package to detect footprints of selection in genome-wide SNP data from haplotype structure. Bioinformatics, 28(8), 1176-1177.

    Article  CAS  PubMed  Google Scholar 

  • Gentry, A., Clutton-Brock, J., & Groves, C. P. (2004). The naming of wild animal species and their domestic derivatives. Journal of Archaeological Science, 31(5), 645-651.

    Article  Google Scholar 

  • Ghildiyal, K., Panigrahi, M., Kumar, H., Rajawat, D., Nayak, S. S., Lei, C., Bhushan, B., & Dutt, T. (2023a). Selection signatures for fiber production in commercial species: A review. Animal Genetics, 54(1), 3–23. https://doi.org/10.1111/age.13272

    Article  PubMed  Google Scholar 

  • Ghildiyal, K., Nayak, S.S., Rajawat, D., Sharma, A., Chottaray, S., Bhushan, B., Dutt, T. and Panigrahi, M., 2023b. Genomic insights into the conservation of wild and animal diversity: A Review. Gene, p.147719.

  • Grabek, K. R., Cooke, T. F., Epperson, L. E., Spees, K. K., Cabral, G. F., Sutton, S. C., ... & Bustamante, C. D. (2017). Genetic architecture drives seasonal onset of hibernation in the 13-lined ground squirrel. bioRxiv, 222307.

  • Gu JJ, Orr N, Park SD, Katz LM, Sulimova G, MacHugh DE, Hill EW. 2009. A Genome scanfor positive selection in thoroughbred horses. PLoS ONE 4: e5767.

    Article  PubMed  PubMed Central  Google Scholar 

  • Jia, L., Chu, H., Wu, D., Feng, M., & Zhao, L. (2014). Role of calmodulin in thermotolerance. Plant signaling & behavior, 9(6), e28887.

    Article  Google Scholar 

  • Joost, S., Bonin, A., Bruford, M. W., Després, L., Conord, C., Erhardt, G., & Taberlet, P. (2007). A spatial analysis method (SAM) to detect candidate loci for selection: towards a landscape genomics approach to adaptation. Molecular ecology, 16(18), 3955-3969.

    Article  CAS  PubMed  Google Scholar 

  • Kijas, J. W., Lenstra, J. A., Hayes, B., Boitard, S., Porto Neto, L. R., San Cristobal, M., ...& International Sheep Genomics Consortium. (2012). Genome-wide analysis of the world’s sheep breeds reveals high levels of historic mixture and strong recent selection. PLoS biology, 10(2), e1001258.

  • Kullo IJ, Ding K. Patterns of population differentiation of candidate genes for cardiovascular disease. BMC Genet. 2007;8:48.

    Article  PubMed  PubMed Central  Google Scholar 

  • Kumar, H., Panigrahi, M., Panwar, A., Rajawat, D., Nayak, S. S., Saravanan, K. A., Kaisa, K., Parida, S., Bhushan, B., Dutt, T. 2022. Machine-Learning Prospects for Detecting Selection Signatures Using Population Genomics Data. J. Comput. Biol. https://doi.org/10.1089/cmb.2021.0447

    Article  PubMed  Google Scholar 

  • Kumar, H., Panigrahi, M., Saravanan, K. A., Rajawat, D., Parida, S., Bhushan, B., Gaur, G. K., Dutt, T., Mishra, B. P., Singh, R. K., 2021. Genome-wide detection of copy number variations in Tharparkar cattle. Anim. Biotechnol. 1–8. https://doi.org/10.1080/10495398.2021.1942027

  • Kumar, H., Panigrahi, M., G. Strillacci, M., Sonejita Nayak, S., Rajawat, D., Ghildiyal, K., Bhushan, B. and Dutt, T., 2023. Detection of genome-wide copy number variation in Murrah buffaloes. Animal Biotechnology, pp.1–13.

  • Lewontin RC, Krakauer J. Distribution of gene frequency as a test of the theory of the selective neutrality of polymorphisms. Genetics. 1973;74:175–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Li, R.; Li, H.; Chen, R.; Chong, Q.; Xiao, H.; Chen, S. Genome-Wide Scan of Selection Signatures in Dehong Humped Cattle for Heat Tolerance and Disease Resistance. Anim. Genet. 2020, 51, 292–299.

    Article  CAS  PubMed  Google Scholar 

  • Li, L., Yu, S., Chen, J., Quan, M., Gao, Y., & Li, Y. (2022). miR-15a and miR-20b sensitize hepatocellular carcinoma cells to sorafenib through repressing CDC37L1 and consequent PPIA downregulation. Cell Death Discovery, 8(1), 297.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Luikart G, England PR, Tallmon D, Jordan S, Taberlet P. 2003. The power and promise of population genomics: from genotyping to genome typing. Nat Rev Genet. 4:981–994.

    Article  CAS  PubMed  Google Scholar 

  • Luna-Nevárez, G., Pendleton, A. L., Luna-Ramirez, R. I., Limesand, S. W., Reyna-Granados, J. R., & Luna-Nevárez, P. (2021). Genome-wide association study of a thermo-tolerance indicator in pregnant ewes exposed to an artificial heat-stressed environment. Journal of Thermal Biology, 101, 103095.

    Article  PubMed  Google Scholar 

  • Lynch, M., Ackerman, M., Gout, JF. et al. Genetic drift, selection and the evolution of the mutation rate. Nat Rev Genet 17, 704–714 (2016). https://doi.org/10.1038/nrg.2016.104

    Article  CAS  PubMed  Google Scholar 

  • MacCallum, C., & Hill, E. (2006). Being positive about selection. PLoS biology, 4(3), e87.

    Article  PubMed  PubMed Central  Google Scholar 

  • McQuillan R, Leutenegger AL, Abdel-Rahman R, et al. Runs of homozygosity in European populations. Am J Hum Genet. 2008;83(3):359–372.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • L. Min, S. Zhao, H. Tian, X. Zhou, Y. Zhang, S. Li, H. Yang, N. Zheng, J. Wang, Metabolic responses and “omics” technologies for elucidating the effects of heat stress in dairy cows, Int. J. Biometeorol. 61 (2016) 1149–1158, https://doi.org/10.1007/s00484-016-1283-z.

    Article  PubMed  PubMed Central  Google Scholar 

  • Nayak, S.S., Kumar, H., Rajawat, D., Saravanan, K.A., Panwar, A., Ghildiyal, K., Smaraki, N., Jogi, H.R., Sharma, A., Bhushan, B. and Dutt, T., Panigrahi, M., 2023a. Coalescence: An anti-clockwise travel.

  • Nayak, S.S., Panigrahi, M., Kumar, H., Rajawat, D., Sharma, A., Bhushan, B. and Dutt, T., 2023b. Evidence for selective sweeps in the MHC gene repertoire of various cattle breeds. Animal Biotechnology, pp.1–7.

  • Nayak, S.S., Panigrahi, M., Rajawat, D., Ghildiyal, K., Sharma, A., Parida, S., Bhushan, B., Mishra, B.P. and Dutt, T., 2023c. Comprehensive selection signature analyses in dairy cattle exploiting purebred and crossbred genomic data. Mammalian Genome, pp.1–17.

  • Neafsey, D. E., Schaffner, S. F., Volkman, S. K., Park, D., Montgomery, P., Milner, D. A., ... & Wirth, D. F. (2008). Genome-wide SNP genotyping highlights the role of natural selection in Plasmodium falciparumpopulation divergence. Genome biology, 9(12), 1–16.

  • Nielsen R, Hellmann I, Hubisz M, Bustamante C, Clark AG. 2007. Recent and ongoingselection in the human genome.Nat Rev Genet. 8: 857–868.

  • Pal, D., Panigrahi, M., Chhotaray, S., Kumar, H., Nayak, S.S., Rajawat, D., Parida, S., Gaur, G.K., Dutt, T. and Bhushan, B., 2022. Unraveling genetic admixture in the Indian crossbred cattle by different approaches using Bovine 50K BeadChip. Tropical Animal Health and Production, 54(2), p.135.

    Article  PubMed  Google Scholar 

  • Panigrahi, M., Kumar, H., Nayak, S.S., Rajawat, D., Parida, S., Bhushan, B., Sharma, A. and Dutt, T., 2022a. Molecular characterization of CRBR2 fragment of TLR4 gene in association with mastitis in Vrindavani cattle. Microbial Pathogenesis, 165, p.105483.

    Article  CAS  PubMed  Google Scholar 

  • Panigrahi, M., Kumar, H., Saravanan, K. A., Rajawat, D., Sonejita Nayak, S., Ghildiyal, K., Kaisa, K., Parida, S., Bhushan, B., & Dutt, T. (2022b). Trajectory of livestock genomics in South Asia: A comprehensive review. Gene, 843(146808), 146808. https://doi.org/10.1016/j.gene.2022.146808

    Article  CAS  PubMed  Google Scholar 

  • Panigrahi, M., Rajawat, D., Nayak, S.S., Ghildiyal, K., Sharma, A., Jain, K., Lei, C., Bhushan, B., Mishra, B.P. and Dutt, T., 2023. Landmarks in the history of selective sweeps. Animal Genetics. https://doi.org/10.1111/age.13355

    Article  PubMed  Google Scholar 

  • Pirkkala, L., Nykänen, P., & Sistonen, L. E. A. (2001). Roles of the heat shock transcription factors in regulation of the heat shock response and beyond. The FASEB Journal, 15(7), 1118-1131.

    Article  CAS  PubMed  Google Scholar 

  • Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A., Bender, D., and Sham, P. C. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics, 81(3), 559-575.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Qanbari S, Pimentel ECG, Tetens J, Thaller G, Lichtner P, Sharifi AR, Simianer H. 2010. Agenome-wide scan for signatures of recent selection in Holstein cattle. Anim Genet. 41:377-389

    Article  CAS  PubMed  Google Scholar 

  • Rajawat, D., Panigrahi, M., Kumar, H., Nayak, S. S., Saravanan, K. A., Bhushan, B., & Dutt, T. (2022b). Revealing genomic footprints of selection for fiber and production traits in three Indian sheep breeds. Journal of Natural Fibers, 19(16), 14963–14974. https://doi.org/10.1080/15440478.2022.2069198

    Article  CAS  Google Scholar 

  • Rajawat, D., Panigrahi, M., Kumar, H., Nayak, S. S., Parida, S., Bhushan, B., Gaur, G. K., Dutt, T., & Mishra, B. P. (2022a). Identification of important genomic footprints using eight different selection signature statistics in domestic cattle breeds. Gene, 816.

  • Rajawat, D., Panigrahi, M., Nayak, S.S., Ghildiyal, K., Sharma, A., Kumar, H., Parida, S., Bhushan, B., Gaur, G.K., Mishra, B.P. and Dutt, T., 2023. Uncovering genes underlying coat color variation in indigenous cattle breeds through genome-wide positive selection. Animal Biotechnology, pp.1–14.

  • Randhawa I.A.S., Khatkar M.S., Thomson P.C. & Raadsma H.W. (2014) Composite selection signals can localize the trait specific genomic regions in multi-breed populations of cattle and sheep. BMC Genetics 15, 1601–8.

    Article  Google Scholar 

  • Saadeldin, I. M., Swelum, A. A. A., Elsafadi, M., Mahmood, A., Osama, A., Shikshaky, H., ... & Magdeldin, S. (2020). Thermotolerance and plasticity of camel somatic cells exposed to acute and chronic heat stress. Journal of advanced research, 22, 105–118.

  • Sabeti PC, Schaffner SF, Fry B, Lohmueller J, Varilly P, Shamovsky O, Palma A, Mikkelsen TS, Altshuler D, Lander ES. (2006). Positive natural selection in the human lineage. Science, 312(5780), 1614-20. https://doi.org/10.1126/science.1124309.

    Article  CAS  PubMed  Google Scholar 

  • Sabeti PC, Varilly P, Fry B, Lohmueller J, Hostetter E, et al (2007). Genome-wide detection and characterization of positive selection in human populations. Nature, 449, 913-918. https://doi.org/10.1038/nature06250.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Saravanan, K. A., Panigrahi, M., Kumar, H., Parida, S., Bhushan, B., Gaur, G. K., ... & Singh, R. K. (2020). Genome-wide assessment of genetic diversity, linkage disequilibrium and haplotype block structure in Tharparkar cattle breed of India. Animal biotechnology, 33(2), 297–311.

  • Saravanan, K. A., Panigrahi, M., Kumar, H., Parida, S., Bhushan, B., Gaur, G. K., ... & Singh, R. K. (2021). Genomic scans for selection signatures revealed candidate genes for adaptation and production traits in a variety of cattle breeds. Genomics, 113(3), 955–963.

  • Saravanan, K. A., Panigrahi, M., Kumar, H., Nayak, S. S., Rajawat, D., Bhushan, B., & Dutt, T. (2022a). Progress and future perspectives of livestock genomics in India: a mini-review. Animal Biotechnology, 1–9. https://doi.org/10.1080/10495398.2022.2056046

  • Saravanan, K. A., Rajawat, D., Kumar, H., Nayak, S. S., Bhushan, B., Dutt, T., & Panigrahi, M. (2022b). Signatures of selection in riverine buffalo populations revealed by genome-wide SNP data. Animal Biotechnology, 1–12. https://doi.org/10.1080/10495398.2022.2145292

  • Savalia, K. B., Ahlawat, A. R., Gamit, V. V., Parikh, S. S., & Verma, A. D. (2019). Recently recognized indigenous cattle breeds of India: A review. International Journal of Current Microbiology and Applied Sciences, 8(12), 161-168.

    Article  Google Scholar 

  • Schafer C, Steffen H, Krzykowski KJ, Goke B, Groblewski GE. CRHSP-24 phosphorylation is regulated by multiple signaling pathways in pancreatic acinar cells. Am J Physiol Gastrointest Liver Physiol. 2003;285(4):G726–34.

    Article  PubMed  Google Scholar 

  • Stella A, Ajmone-Marsan P, Lazzari B, Boettcher P. 2010. Identification of selectionsignatures in cattle breeds selected for dairy production.Genetics 185: 1451–1498

  • Sved JA, Feldman MW. Correlation and probability methods for one and two loci. Theor Popul Biol. 1973; 4(1):129–132.

    Article  CAS  PubMed  Google Scholar 

  • Szpiech, Z.A., Xu, J., Pemberton, T.J., Peng, W., Zöllner, S., Rosenberg, N.A. and Li, J.Z. (2013). Long runs of homozygosity are enriched for deleterious variation. The American Journal of Human Genetics, 93(1), 90-102.

    Article  CAS  PubMed  Google Scholar 

  • Tajima F. 1989. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123: 585–595.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tang, S., Yin, B., Xu, J., & Bao, E. (2018). Rosemary reduces heat stress by inducing CRYAB and HSP70 expression in broiler chickens. Oxidative Medicine and Cellular Longevity, 2018.

  • The Bovine HapMap Consortium, Gibbs, R. A., Taylor, J. F., Tassel, C. V., Barendse, W., Eversole, K. A., Gill, C. A., and Green, R. D. 2009. Genome-wide survey of SNP variation uncovers the genetic structure of cattle breeds. Science.324 (5926): 528-532.

    Article  PubMed Central  Google Scholar 

  • van Marle-Köster, E., & Visser, C. (2018). Genetic improvement in South African livestock: can genomics bridge the gap between the developed and developing sectors?. Frontiers in Genetics, 9, 331.

    Article  PubMed  PubMed Central  Google Scholar 

  • Voight BF, Kudaravalli S, Wen X, Pritchard JK: A map of recent positive selection in the human genome. PLoS Biology. 2006, 4: e72–https://doi.org/10.1371/journal.pbio.0040072.

  • Wang, X.; Ju, Z.; Jiang, Q.; Zhong, J.; Liu, C.; Wang, J.; Hoff, J.L.; Schnabel, R.D.; Zhao, H.; Gao, Y.; et al. Introgression, Admixture and Selection Facilitate Genetic Adaptation to High-Altitude Environments in Cattle. Genomics 2021, 113, 1491–1503.

    Article  CAS  PubMed  Google Scholar 

  • Weir B.S. & Cockerham C.C. (1984) Estimating F-statistics for the analysis of population structure. Evolution 38, 1358–70.

    CAS  PubMed  Google Scholar 

  • Williamson, S. H., Hubisz, M. J., Clark, A. G., Payseur, B. A., Bustamante, C. D., & Nielsen, R. (2007). Localizing recent adaptive evolution in the human genome. PLoS genetics, 3(6), e90.

    Article  PubMed  PubMed Central  Google Scholar 

  • Yang, J., Jin, Z.B., Chen, J., Huang, X.F., Li, X.M., Liang, Y.B., Mao, J.Y., Chen, X., Zheng, Z., Bakshi, A. and Zheng, D.D., 2017. Genetic signatures of high-altitude adaptation in Tibetans. Proceedings of the National Academy of Sciences, 114(16), 4189-4194.

    Article  CAS  Google Scholar 

  • Zhao, Qb., Oyelami, F.O., Qadri, Q.R. et al. Identifying the unique characteristics of the Chinese indigenous pig breeds in the Yangtze River Delta region for precise conservation. BMC Genomics 22, 151 (2021).

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Acknowledgements

Support received from the National Agricultural Science Fund (NASF), Indian Council of Agricultural Research, New Delhi, India, for carrying out this work is duly acknowledged. The authors wish to thank the Director and Joint Director (Research), ICAR-Indian Veterinary Research Institute, Bareilly, India, for providing the necessary facilities during this study.

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  1. Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, 243122, UP, India

    Sonali Sonejita Nayak, Manjit Panigrahi, Divya Rajawat, Kanika Ghildiyal, Anurodh Sharma, Karan Jain & Bharat Bhushan

  2. Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, 243122, UP, India

    Triveni Dutt

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  1. Sonali Sonejita Nayak
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Contributions

SSN: methodology and writing—original draft. MP: conceptualization, methodology, and writing—review and editing. DR: writing—review and editing. KG: review and editing. AS: writing—review and editing. KJ: writing—review and editing. BB and TD: resources and supervision.

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Correspondence to Manjit Panigrahi.

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All the procedures performed in this study involving animals were following the ethical standards of the Institutional Animal Ethics Committee (IAEC) of ICAR-IVRI, Izatnagar, Bareilly, India (NO.F.1–53/2018–19/J. D(R)).

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The authors declare no competing interests.

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Nayak, S.S., Panigrahi, M., Rajawat, D. et al. Deciphering climate resilience in Indian cattle breeds by selection signature analyses. Trop Anim Health Prod 56, 46 (2024). https://doi.org/10.1007/s11250-023-03879-8

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  • DOI: https://doi.org/10.1007/s11250-023-03879-8

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