Genome-wide single nucleotide polymorphism (SNP) identification in Red Sindhi cattle (Bos indicus) using whole-genome sequencing

Authors

  • Roohi Kanwal Department of Zoology, University of Karachi
  • Nazeer Hussain Kolhoro Sindh Institute of Animal Health (SIAH) Karachi
  • Sitwat Zehra Dr.A.Q.Khan Institute of Biotechnology and Genetic Engineering (KIBGE), Univeristy of Karachi
  • Benazir Kanwal Sindh Institute of Animal Health (SIAH) Karachi
  • Ehtesham Asif Sindh Institute of Animal Health (SIAH) Karachi
  • Saima Saleem Dr.A.Q.Khan Institute of Biotechnology and Genetic Engineering (KIBGE), Univeristy of Karachi

DOI:

https://doi.org/10.5281/zenodo.22848102

Keywords:

Red Sindhi cattle; Bos indicus; whole-genome sequencing; SNPs; genomic variation; genetic diversity

Abstract

Red Sindhi cattle (Bos indicus) is an important indigenous breed with characteristics of adaptability, survival in tropical conditions, and disease resistance. Although they are agriculturally important, this breed has far less genomic information than taurine cattle. In the current study, the researchers sought to identify all single-nucleotide polymorphisms (SNPs) in the genome of Red Sindhi cattle using whole-genome sequencing (WGS) with next-generation sequencing (NGS). We performed paired-end whole-genome sequencing on genomic DNA from Red Sindhi cattle. The reads were sequenced and mapped to the Bos taurus reference genome (ARS-UCD1.2) using the BWA-MEM algorithm, and then the variants were called with the FreeBayes pipeline. We selected high-confidence variants based on stringent quality-filtering criteria. We finally obtained a filtered set of 5,932 high-confidence SNPs spread across the bovine genome. The average sequencing depth ranged from 30 to 32×, which is sufficient for reliable variant detection and genotype calling. Many of the remaining variants were heterozygous, indicating allelic variation in the samples analysed. Analysis of chromosome distribution showed an uneven distribution of SNPs across chromosomes, with chromosomes X and 14 having a relatively greater number of SNPs. This study provides a preliminary genome-wide SNP resource for Red Sindhi cattle and a foundation for future population genomics, comparative genomics, and functional annotation studies in indigenous indicine cattle breeds. Further large-scale studies with broader population sampling and more sophisticated population genomics analyses are needed to fully describe the breed's allelic variation.

References

Ajmone-Marsan, P., Garcia, J. F., & Lenstra, J. A. (2010). On the origin of cattle: How aurochs became cattle and colonized the world. Evolutionary Anthropology: Issues, News, and Reviews, 19(4), 148–157. https://doi.org/10.1002/evan.20250

Alkan, C., Sajjadian, S., & Eichler, E. E. (2011). Limitations of next-generation genome sequence assembly. Nature Methods, 8(1), 61–65. https://doi.org/10.1038/nmeth.1527

Allendorf, F. W., Luikart, G., & Aitken, S. N. (2010). Conservation and the genetics of populations (2nd ed.). Wiley-Blackwell.

Bovine Genome Sequencing and Analysis Consortium. (2009). The genome sequence of taurine cattle: A window to ruminant biology and evolution. Science, 324(5926), 522–528. https://doi.org/10.1126/science.1169588

Chaisson, M. J. P., Sanders, A. D., Zhao, X., Malhotra, A., Porubsky, D., Rausch, T., Gardner, E. J., Rodriguez, O. L., Guo, L., Collins, R. L., Fan, X., Wen, J., Handsaker, R. E., Fairley, S., Kronenberg, Z. N., Kong, X., Hormozdiari, F., Lee, D., Wenger, A. M., … Lee, C. (2019). Multi-platform discovery of haplotype-resolved structural variation in human genomes. Nature Communications, 10, 1784. https://doi.org/10.1038/s41467-018-08148-z

Daetwyler, H. D., Capitan, A., Pausch, H., Stothard, P., van Binsbergen, R., Brøndum, R. F., Liao, X., Djari, A., Rodriguez, S. C., Grohs, C., Esquerré, D., Bouchez, O., Rossignol, M. N., Klopp, C., Rocha, D., Fritz, S., Eggen, A., Bowman, P. J., Coote, D., … Hayes, B. J. (2014). Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nature Genetics, 46(8), 858–865. https://doi.org/10.1038/ng.3034

FAO. (2015). The second report on the state of the world's animal genetic resources for food and agriculture. Food and Agriculture Organization of the United Nations. http://www.fao.org/3/a-i4787e.pdf

Hayes, B. J., MacLeod, I. M., Daetwyler, H. D., Bowman, P. J., Chamberlain, A. J., Vander Jagt, C., Capitan, A., Pausch, H., Stothard, P., Liao, X., Schrooten, C., Mullaart, E., Fries, R., Guldbrandtsen, B., Lund, M. S., Boichard, D. A., Veerkamp, R. F., VanTassell, C. P., Gredler, B., … Goddard, M. E. (2014). Genomic prediction from whole genome sequence in livestock: The 1000 Bull Genomes Project. Proceedings of the 10th World Congress of Genetics Applied to Livestock Production

Hayes, B. J., & Daetwyler, H. D. (2019). 1000 Bull Genomes Project to map simple and complex genetic traits in cattle: Applications and outcomes. Annual Review of Animal Biosciences, 7, 89–102. https://doi.org/10.1146/annurev-animal-020518-115023

Hayes, B. J., & Goddard, M. E. (2010). Genome-wide association and genomic selection in animal breeding. Genome, 53(11), 876–883. https://doi.org/10.1139/G10-076

Kim, J., Hanotte, O., Mwai, O. A., Dessie, T., & Park, S. (2017). Adaptation of cattle to new environments: Insights from genomic studies. Nature Reviews Genetics, 18(9), 581–594. https://doi.org/10.1038/nrg.2017.38

Kumar, S., Gupta, J., Kumar, N., Dikshit, K., Navani, N., & Jain, P. K. (2016). Allelic variation and population structure of indigenous cattle breeds of India using microsatellite markers. Animal Biotechnology, 27(3), 1–10. https://doi.org/10.1080/10495398.2015.1107605

Matukumalli, L. K., Lawley, C. T., Schnabel, R. D., Taylor, J. F., Allan, M. F., Heaton, M. P., O’Connell, J., Moore, S. S., Smith, T. P. L., Sonstegard, T. S., & Van Tassell, C. P. (2009). Development and characterization of a high density SNP genotyping assay for cattle. PLOS ONE, 4(4), e5350. https://doi.org/10.1371/journal.pone.0005350

McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R. S., Thormann, A., Flicek, P., & Cunningham, F. (2016). The Ensembl Variant Effect Predictor. Genome Biology, 17, 122. https://doi.org/10.1186/s13059-016-0974-4

Metzker, M. L. (2010). Sequencing technologies—the next generation. Nature Reviews Genetics, 11(1), 31–46. https://doi.org/10.1038/nrg2626

Nielsen, R., Paul, J. S., Albrechtsen, A., & Song, Y. S. (2011). Genotype and SNP calling from next-generation sequencing data. Nature Reviews Genetics, 12(6), 443–451. https://doi.org/10.1038/nrg2986

Porto-Neto, L. R., Sonstegard, T. S., & Eggen, A. (2013a). The use of genomics in cattle adaptation studies. Genetics Selection Evolution, 45(1), 37. https://doi.org/10.1186/1297-9686-45-37

Porto-Neto, L. R., Sonstegard, T. S., & Lee, S. H. (2013b). Genomic signatures of selection in cattle. Genetics Selection Evolution, 45, 37. https://doi.org/10.1186/1297-9686-45-37

Rehman, Z. U., Khan, M. S., Bhatti, S. A., & Khan, M. A. (2017). Phenotypic characterization and production performance of Red Sindhi cattle. Journal of Animal Science and Technology, 59, 25. https://doi.org/10.1186/s40781-017-0141-0

Shendure, J., & Ji, H. (2008). Next-generation DNA sequencing. Nature Biotechnology, 26(10), 1135–1145. https://doi.org/10.1038/nbt1486

Upadhyay, M. (2019). Genomic insights into adaptation in indicine cattle. BMC Genomics, 20, 1–15. https://doi.org/10.1186/s12864-019-5895-4

Van der Auwera, G. A., Carneiro, M. O., Hartl, C., Poplin, R., del Angel, G., Levy-Moonshine, A., Jordan, T., Shakir, K., Roazen, D., Thibault, J., Banks, E., Garimella, K. V., Altshuler, D., Gabriel, S., & DePristo, M. A. (2013). From FastQ data to high-confidence variant calls: The Genome Analysis Toolkit best practices pipeline. Current Protocols in Bioinformatics, 43(1), 11.10.1–11.10.33. https://doi.org/10.1002/0471250953.bi1110s43

Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017). 10 years of GWAS discovery: Biology, function, and translation. American Journal of Human Genetics, 101(1), 5–22. https://doi.org/10.1016/j.ajhg.2017.06.005

Yates, A. D., Achuthan, P., Akanni, W., Allen, J., Allen, J., Alvarez-Jarreta, J., Amode, M. R., Armean, I. M., Azov, A. G., Bennett, R., Bhai, J., Billis, K., Boddu, S., Marugán, J. C., Cummins, C., Davidson, C., Dodiya, K., Fatima, R., Gall, A., … Flicek, P. (2020). Ensembl 2020. Nucleic Acids Research, 48(D1), D682–D688. https://doi.org/10.1093/nar/gkz966

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Published

2026-08-11

How to Cite

Roohi Kanwal, Nazeer Hussain Kolhoro, Sitwat Zehra, Benazir Kanwal, Ehtesham Asif, & Saima Saleem. (2026). Genome-wide single nucleotide polymorphism (SNP) identification in Red Sindhi cattle (Bos indicus) using whole-genome sequencing. Journal of Wildlife and Biodiversity, 10(2), 150–166. https://doi.org/10.5281/zenodo.22848102