Animal Bioscience

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
권호 표지
DOI QR코드

DOI QR Code

Genetic diversity and population structure in five Inner Mongolia cashmere goat populations using whole-genome genotyping

  • Tao Zhang (College of Animal Science, Inner Mongolia Agricultural University) ;
  • Zhiying Wang (College of Animal Science, Inner Mongolia Agricultural University) ;
  • Yaming Li (College of Animal Science, Inner Mongolia Agricultural University) ;
  • Bohan Zhou (College of Animal Science, Inner Mongolia Agricultural University) ;
  • Yifan Liu (College of Animal Science, Inner Mongolia Agricultural University) ;
  • Jinquan Li (Inner Mongolia Key Laboratory of Sheep and Goat Genetics Breeding and Reproduction) ;
  • Ruijun Wang (College of Animal Science, Inner Mongolia Agricultural University) ;
  • Qi Lv (College of Animal Science, Inner Mongolia Agricultural University) ;
  • Chun Li (College of Animal Science and Technology, Inner Mongolia Minzu University) ;
  • Yanjun Zhang (College of Animal Science, Inner Mongolia Agricultural University) ;
  • Rui Su (College of Animal Science, Inner Mongolia Agricultural University)
  • Received : 2023.10.17
  • Accepted : 2024.01.26
  • Published : 2024.07.01
Download PDF
⟨ Previous Next ⟩

Abstract

Objective: As a charismatic species, cashmere goats have rich genetic resources. In the Inner Mongolia Autonomous Region, there are three cashmere goat varieties named and approved by the state. These goats are renowned for their high cashmere production and superior cashmere quality. Therefore, it is vitally important to protect their genetic resources as they will serve as breeding material for developing new varieties in the future. Methods: Three breeds including Inner Mongolia cashmere goats (IMCG), Hanshan White cashmere goats (HS), and Ujimqin white cashmere goats (WZMQ) were studied. IMCG were of three types: Aerbas (AEBS), Erlangshan (ELS), and Alashan (ALS). Nine DNA samples were collected for each population, and they were genomically re-sequenced to obtain high-depth data. The genetic diversity parameters of each population were estimated to determine selection intensity. Principal component analysis, phylogenetic tree construction and genetic differentiation parameter estimation were performed to determine genetic relationships among populations. Results: Samples from the 45 individuals from the five goat populations were sequenced, and 30,601,671 raw single nucleotide polymorphisms (SNPs) obtained. Then, variant calling was conducted using the reference genome, and 17,214,526 SNPs were retained after quality control. Individual sequencing depth of individuals ranged from 21.13× to 46.18×, with an average of 28.5×. In the AEBS, locus polymorphism (79.28) and expected heterozygosity (0.2554) proportions were the lowest, and the homologous consistency ratio (0.1021) and average inbreeding coefficient (0.1348) were the highest, indicating that this population had strong selection intensity. Conversely, ALS and WZMQ selection intensity was relatively low. Genetic distance between HS and the other four populations was relatively high, and genetic exchange existed among the other four populations. Conclusion: The Inner Mongolia cashmere goat (AEBS type) population has a relatively high selection intensity and a low genetic diversity. The IMCG (ALS type) and WZMQ populations had relatively low selection intensity and high genetic diversity. The genetic distance between HS and the other four populations was relatively high, with a moderate degree of differentiation. Overall, these genetic variations provide a solid foundation for resource identification of Inner Mongolia Autonomous Region cashmere goats in the future.

Keywords

Acknowledgement

Thanks are due to Libing He for assistance with the experiments and to Oljibilig Chen for valuable discussion.

References

  1. Zheng Z, Wang X, Li M, et al. The origin of domestication genes in goats. Sci Adv 2020;6:eaaz5216. https://doi.org/10.1126/sciadv.aaz5216
  2. Islam R, Liu X, Gebreselassie G, Abied A, Ma Q, Ma Y. Genome-wide association analysis reveals the genetic locus for high reproduction trait in Chinese Arbas Cashmere goat. Genes Genomics 2020;42:893-9. https://doi.org/10.1007/s13258-020-00937-5 
  3. Li X, Su R, Wan W, et al. Identification of selection signals by large-scale whole-genome resequencing of cashmere goats. Sci Rep 2017;7:15142. https://doi.org/10.1038/s41598-017-15516-0 
  4. Yang X, Li Z. Thoughts on current situation and development of cashmere industry in China. Wool Text J 2017;45:84-7. https://doi.org/10.19333/j.mfkj.20161102511104 
  5. Zhang Y, Wang Z, Lei H, et al. Estimates of genetic parameters and genetic changes for fleece traits in Inner Mongolia cashmere goats. Small Rumin Res 2014;117:41-6. https://doi.org/10.1016/j.smallrumres.2013.10.011 
  6. Su R, Gong G, Zhang L, et al. Screening the key genes of hair follicle growth cycle in Inner Mongolian Cashmere goat based on RNA sequencing. Arch Anim Breed 2020;63:155-64. https://doi.org/10.5194/aab-63-155-2020 
  7. Toro MA, Caballero A. Characterization and conservation of genetic diversity in subdivided populations. Philos Trans R Soc Lond B Biol Sci 2005;360:1367-78. https://doi.org/10.1098/rstb.2005.1680 
  8. Crawford A, Fassett RG, Geraghty DP, et al. Relationships between single nucleotide polymorphisms of antioxidant enzymes and disease. Gene 2012;501:89-103. https://doi.org/10.1016/j.gene.2012.04.011 
  9. Grover A, Sharma PC. Development and use of molecular markers: past and present. Crit Rev Biotechnol 2016;36:290-302. https://doi.org/07388551.2014.959891 
  10. Guo F, Ye Y, Zhu K, et al. Genetic diversity, population structure, and environmental adaptation signatures of chinese coastal hard-shell mussel Mytilus coruscus revealed by whole-genome sequencing. Int J Mol Sci 2023;24:13641. https://doi.org/10.3390/ijms241713641 
  11. Al-Atiyat RM, Alobre MM, Aljumaah RM, Alshaikh MA. Microsatellite based genetic diversity and population structure of three Saudi goat breeds. Small Rumin Res 2015;130:90-4. https://doi.org/10.1016/j.smallrumres.2015.07.027 
  12. Deniskova TE, Dotsev AV, Selionova MI, et al. Population structure and genetic diversity of 25 Russian sheep breeds based on whole-genome genotyping. Genet Sel Evol 2018;50:29. https://doi.org/10.1186/s12711-018-0399-5 
  13. Jung Y, Han D. BWA-MEME: BWA-MEM emulated with a machine learning approach. Bioinformatics 2022;38:2404-13. https://doi.org/10.1093/bioinformatics/btac137 
  14. Meseret S, Mekonnen YA, Brenig B, et al. Genetic diversity and population structure of six ethiopian cattle breeds from different geographical regions using high density single nucleotide polymorphisms. Livest Sci 2020;234:103979. https://doi.org/10.1016/j.livsci.2020.103979 
  15. Danecek P, McCarthy SA. BCFtools/csq: haplotype-aware variant consequences. Bioinformatics 2017;33:2037-9. https://doi.org/10.1093/bioinformatics/btx100 
  16. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 2010;38:e164. https://doi.org/10.1093/nar/gkq603 
  17. Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA. Stacks: an analysis tool set for population genomics. Mol Ecol 2013;22:3124-40. https://doi.org/10.1111/mec.12354 
  18. Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559-75. https://doi.org/10.1086/519795 
  19. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 2011;88:76-82. https://doi.org/10.1016/j.ajhg.2010.11.011 
  20. Chan BKC. Data analysis using R programming. Adv Exp Med Biol 2018;1082:47-122. https://doi.org/10.1007/978-3-319-93791-5_2 
  21. Montana G, Hoggart C. Statistical software for gene mapping by admixture linkage disequilibrium. Brief Bioinform 2007;8:393-5. https://doi.org/10.1093/bib/bbm035 
  22. Deniskova TE, Dotsev AV, Selionova MI, et al. SNP-based genotyping provides insight into the west asian origin of russian local goats. Front Genet 2021;12:708740. https://doi.org/10.3389/fgene.2021.708740 
  23. Danecek P, Auton A, Abecasis G, et al. The variant call format and VCFtools. Bioinformatics 2011;27:2156-8. https://doi.org/10.1093/bioinformatics/btr330 
  24. Zhao YH, Wang MY, Su R, et al. The genetic diversity analysis on 4 Inner Mongolian Cashmere goats. Hubei Nong Ye Ke Xue 2012;8:1681-3. 
  25. Botstein D, White RL, Skolnick M, Davis RW. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet 1980;32:314-31. 
  26. Weir BS, Cockerham CC. Estimating f-statistics for the analysis of population structure. Evolution 1984;38:1358-70. https://doi.org/10.1111/j.1558-5646.1984.tb05657.x 
  27. Mtileni BJ, Muchadeyi FC, Maiwashe A, et al. Genetic diversity and conservation of South African indigenous chicken populations. J Anim Breed Genet 2011;128:209-18. https://doi.org/10.1111/j.1439-0388.2010.00891.x 
  28. Wang X, Wang C, Huang M, et al. Genetic diversity, population structure and phylogenetic relationships of three indigenous pig breeds from Jiangxi Province, China, in a worldwide panel of pigs. Anim Genet 2018;49:275-83. https://doi.org/10.1111/age.12687 
  29. Zhang M, Han W, Tang H, et al. Genomic diversity dynamics in conserved chicken populations are revealed by genome-wide SNPs. BMC Genomics 2018;19:598. https://doi.org/10.1186/s12864-018-4973-6 
  30. Machete JB, Kgwatalala PM, Nsoso SJ, Hlongwane NL, Moreki JC. Genetic diversity and population structure of three strains of Indigenous Tswana chickens and commercial broiler using single nucleotide polymormophic (SNP) markers. J Anim Sci 2021;11:515-31. https://doi.org/10.4236/ojas.2021.114035