DOI QR코드

DOI QR Code

Genome-wide copy number variation regions in indigenous (Bos indicus) cattle breeds of Tamil Nadu, India

  • S. Vani (Department of Animal Genetics and Breeding, Tamil Nadu Veterinary and Animal Sciences University) ;
  • D. Balasubramanyam (Department of Animal Genetics and Breeding, Tamil Nadu Veterinary and Animal Sciences University) ;
  • K. G. Tirumurugaan (Department of Animal Biotechnology, Tamil Nadu Veterinary and Animal Sciences University) ;
  • A. Gopinathan (Department of Animal Genetics and Breeding, Tamil Nadu Veterinary and Animal Sciences University) ;
  • S. M. K. Karthickeyan (Department of Animal Genetics and Breeding, Tamil Nadu Veterinary and Animal Sciences University)
  • 투고 : 2023.12.13
  • 심사 : 2024.07.04
  • 발행 : 2025.03.01

초록

Objective: Identification of large scale structural polymorphisms (copy number variations [CNVs]) of more than 50 bp between the individuals of a species would help in knowing genetic diversity, phenotypic variability, adaptability to tropical environment and disease resistance. Methods: Read depth-based method implemented in CNVnator was used for calling copy number variant regions on sequenced data obtained from whole-genome sequencing from 15 pooled samples belonging to five draught cattle breeds of Tamil Nadu. Results: A total of 11,605 CNV regions (CNVRs) were observed covering a genome size of 18.63 percent. Among these, 11,459 were restricted to autosomes, consisting of 11,013 deletions, 353 duplications and 93 complex events. These CNVRs were annotated to 4,989 candidate genes. A total of 8,291 numbers of CNVRs were shared among the five cattle breeds as also supported by principal component analysis and STRUCTURE analyses and 1,172 CNVRs were breed-specific. Four out of five selected breed-specific CNVRs were validated using real-time polymerase chain reaction. Genes with CNVRs are related to milk production (BTN1A1, ABCA1, and LAP3), disease resistance (TLR4 and DNAH8), adaptability (SOD1, CAST, and SMARCAL1), growth (EGFR, NKAIN3), reproduction (BRWD1 and PDE6D), meat and carcass traits (MAP3K5 and NCAM1) and exterior (ATRN and MITF) traits. Gene enrichment analysis based on the gene list retrieved from the CNVRs disclosed over-represented terms (p<0.01) associated with milk fat production. NETWORK analysis had identified 13 putative candidate genes involved in milk fat percentage, milk fat yield, lactation persistency, milk yield, heat tolerance, calving ease, growth and conformation traits. Conclusion: The genome-wide CNVRs identified in the present study produced genome-wide partial CNV map in indigenous cattle breeds of Tamil Nadu.

키워드

과제정보

We sincerely extend our gratitude to the TANUVAS for providing all the necessary facilities for successful execution of this work.

참고문헌

  1. Ministry of Agriculture, Department of Animal Husbandary and Dairying, and Fisheries. 19th Livestock Census-2012 all India report [Internet]. Krishi Bhavan, New Delhi, India: Department of Animal Husbandry & Dairying; c2012 [cited 2023 Dec 1]. Available from: https://www.dahd.nic.in 
  2. Ministry of Fisheries, Animal Husbandary and Dairying, Department of Animal Husbandary and Dairying, Animal Husbandary statistics division. 20th Livestock Census-2019 all India report [Internet]. Krishi Bhavan, New Delhi, India: Department of Animal Husbandry & Dairying; c2019 [cited 2023 Dec 1]. Available from: https://www.dahd.nic.in 
  3. Oltenacu PA, Broom DM. The impact of genetic selection for increased milk yield on the welfare of dairy cows. Anim Welf 2010;19:39-49. https://doi.org/10.1017/S0962728600002220 
  4. Heaton MP, Harhay GP, Bennett GL, et al. Selection and use of SNP markers for animal identification and paternity analysis in U.S. beef cattle. Mamm Genome 2002;13:272-81. https://doi.org/10.1007/s00335-001-2146-3 
  5. Tian F, Sun, D, Zhang Y. Establishment of paternity testing system using microsatellite markers in Chinese Holstein. J Genet Genom 2008;35:279-84. https://doi.org/10.1016/S1673-8527(08)60040-5 
  6. Edea Z, Dadi H, Dessie T, Lee SH, Kim KS. Genome-wide linkage disequilibrium analysis of indigenous cattle breeds of Ethiopia and Korea using different SNP genotyping BeadChips. Genes Genomics 2015;37:759-65. https://doi.org/10.1007/s13258-015-0304-3 
  7. Nielsen RJ, Paul JS, Albrechtsen A, Song YS. Genotype and SNP calling from next-generation sequencing data. Nat Rev Genet 2011;12:443-51. https://doi.org/10.1038/nrg2986 
  8. Pool R, Waddell K. Exploring horizons for domestic animal genomics. National Academic Press; 2002. https://doi.org/10.17226/10487 
  9. Bickhart DM, Xu L, Hutchison JL, et al. Diversity and population-genetic properties of copy number variations and multi copy genes in cattle. DNA Res 2016;23:253-62. https://doi.org/10.1093/dnares/dsw013 
  10. Pierce MD, Dzama K, Muchadeyi FC. Genetic diversity of seven cattle breeds inferred using copy number variations. Front Genet 2018;9:163. https://doi.org/10.3389/fgene.2018.00163 
  11. Sambrook J, Fritsch EF, Maniatis T. Isolation of DNA from mammalian cells: protocol I. In: Sambrook J, Fritsch EF, Maniatis T, editors. Molecular cloning: a laboratory manual. Cold Spring Harbor Laboratory Press; 1989. pp. 917-9. 
  12. Anand S, Mangano E, Barizzone N, et al. Next generation sequencing of pooled samples: guideline for variants' filtering. Sci Rep 2016;6:33735. https://doi.org/10.1038/srep33735 
  13. Andrews S. FastQC: a quality control tool for high throughput sequence data [Internet]. Cambridge, UK: Babraham Institute; c2017 [cited 2023 Dec 1]. Available from: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ 
  14. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 2016;32:3047-8. https://doi.org/10.1093/bioinformatics/btw354 
  15. Gao Y, Jiang J, Yang S, et al. CNV discovery for milk composition traits in dairy cattle using whole genome resequencing. BMC Genomics 2017;18:265. https://doi.org/10.1186/s12864017-3636-3 
  16. Zhang Y, Hu Y, Wang X, et al. Population structure, and selection signatures underlying high-altitude adaptation inferred from genome-wide copy number variations in Chinese indigenous Cattle. Front Genet 2020;10:1404. https://doi.org/10.3389/fgene.2019.01404 
  17. Jiang L, Jiang J, Yang J, et al. Genome-wide detection of copy number variations using high-density SNP genotyping platforms in Holsteins. BMC Genomics 2013;14:131. https://doi.org/10.1186/1471-2164-14-131 
  18. Liao X, Peng F, Forni S, McLaren D, Plastow G, Stothard P. Whole genome sequencing of Gir cattle for identifying polymorphisms and loci under selection. Genome 2013;56:592-8. https://doi.org/10.1139/gen-2013-0082 
  19. Sudmant PH, Kitzman JO, Antonacci F, et al. Diversity of human copy number variation and multicopy genes. Science 2010;330:641-6. https://doi.org/10.1126/science.1197005 
  20. Mei C, Gui L, Hong J, et al. Insights into adaption and growth evolution: a comparative genomics study on two distinct cattle breeds from Northern and Southern China. Mol Ther Nucleic Acids 2021;23:959-67. https://doi.org/10.1016/j.omtn.2020.12.028 
  21. da Silva JM, Giachetto PF, da Silva LO, et al. Genome-wide copy number variation (CNV) detection in Nelore cattle reveals highly frequent variants in genome regions harboring QTLs affecting production traits. BMC Genomics 2016;17:454. https://doi.org/10.1186/s12864-016-2752-9 
  22. Liu M, Fang L, Liu S, et al. Array CGH-based detection of CNV regions and their potential association with reproduction and other economic traits in Holsteins. BMC Genomics 2019;20:181. https://doi.org/10.1186/s12864-019-5552-1 
  23. Stothard P, Choi JW, Basu U, et al. Whole genome resequencing of Black Angus and Holstein cattle for SNP and CNV discovery. BMC Genomics 2011;12:559. https://doi.org/10.1186/1471-2164-12-559 
  24. Zhang L, Jia S, Yang M, et al. Detection of copy number variations and their effects in Chinese bulls. BMC Genomics 2014;15:480. https://doi.org/10.1186/1471-2164-15-480 
  25. Zhou Y, Utsunomiya YT, Xu L, et al. Comparative analyses across cattle genders and breeds reveal the pitfalls caused by false positive and lineage-differential copy number variations. Sci Rep 2016;6:29219. https://doi.org/10.1038/srep29219 
  26. Hou Y, Liu GE, Bickhart DM, et al. Genomic characteristics of cattle copy number variations. BMC Genomics 2011;12:127. https://doi.org/10.1186/1471-2164-12-127 
  27. Liu GE, Hou Y, Zhu B, et al. Analysis of copy number variations among diverse cattle breeds. Genome Res 2010;20:693-703. https://doi.org/10.1101/gr.105403.110 
  28. Manomohan V, Saravanan R, Pichler R, et al. Legacy of draught cattle breeds of South India: insights into population structure, genetic admixture and maternal origin. PLOS ONE 2021;16:e0246497. https://doi.org/10.1371/journal.pone.0246497 
  29. Shin DH, Lee HJ, Cho S, et al. Deletedcopy number variation of Hanwoo and Holstein using next generation sequencing at the population level. BMC Genomics 2014;15:240. https://doi.org/10.1186/1471-2164-15-240 
  30. Stella A, Ajmone-Marsan P, Lazzari B, Boettcher P. Identification of selection signatures in cattle breeds selected for dairy production. Genetics 2010;185:1451-61. https://doi.org/10.1534/genetics.110.116111 
  31. Kumar RD, Devadasan MJ, Surya T, et al. Genomic diversity and selection sweeps identified in Indian swamp buffaloes reveals it's uniqueness with riverine buffaloes. Genomics 2020;112:2385-92. https://doi.org/10.1016/j.ygeno.2020.01.010 
  32. Koohmaraie M, Kent MP, Shackelford SD, Veiseth E, Wheeler TL. Meat tenderness and muscle growth: is there any relationship? Meat Sci 2002;62:345-52. https://doi.org/10.1016/S0309-1740(02)00127-4 
  33. Cui CY, Yin M, Sima J, et al. Involvement of Wnt, Eda and Shh at defined stages of sweat gland development. Development 2014;141:3752-60. https://doi.org/10.1242/dev.109231 
  34. Li R, Li C, Chen H, et al. Genome-wide scan of selection signatures in Dehong humped cattle for heat tolerance and disease resistance. Anim Genet 2020;51:292-9. https://doi.org/10.1111/age.12896 
  35. Buaban S, Lengnudum K, Boonkum W, Phakdeedindan P. Genome-wide association study on milk production and somatic cell score for Thai dairy cattle using weighted singlestep approach with random regression test-day model. J Dairy Sci 2022;105:468-94. https://doi.org/10.3168/jds.202019826 
  36. Suktitipat B, Naktang C, Mhuantong W, et al. Copy number variation in Thai population. PLOS ONE 2014;9:e104355. https://doi.org/10.1371/journal.pone.0104355 
  37. Gutiérrez-Gil B, Arranz, JJ, Wiener P. An interpretive review of selective sweep studies in Bos taurus cattle populations: identification of unique and shared selection signals across breeds. Front Genet 2015;6:167. https://doi.org/10.3389/fgene.2015.00167 
  38. Choi JW, Lee KT, Liao X, et al. Genome-wide copy number variation in Hanwoo, Black Angus and Holstein cattle. Mamm Genome 2013;24:151-63. https://doi.org/10.1007/s00335013-9449-z 
  39. Jiang L, Jiang J, Wang J, Ding X, Liu J, Zhang Q. Genomewide identification of copy number variations in Chinese Holstein. PLOS ONE 2012;7:e48732. https://doi.org/10.1371/journal.pone.0048732 
  40. Pinto D, Marshall C, Feuk L, Scherer SW. Copy-number variation in control population cohorts. Hum Mol Genet 2007;16:R168-73. https://doi.org/10.1093/hmg/ddm241