Animal Bioscience

  • 제39권1호
  • /
  • Pages.250334.1-250334.14
  • /
  • 2026
  • /
  • 2765-0189(pISSN)
  • /
  • 2765-0235(eISSN)
아세아태평양축산학회 (Asian Australasian Association of Animal Production Societies)
권호 표지
DOI QR코드

DOI QR Code

Population structure and genome-wide association study of body conformation traits of two native goat breeds in China

  • Rong Yang (Guizhou Provincial Breeding Livestock and Poultry Germplasm Determination Center) ;
  • Di Zhou (Guizhou Provincial Breeding Livestock and Poultry Germplasm Determination Center) ;
  • Yanli Lv (Guizhou Provincial Breeding Livestock and Poultry Germplasm Determination Center) ;
  • Xingzhou Tian (College of Animal Science, Guizhou University) ;
  • Liqun Ren (Guizhou Provincial Breeding Livestock and Poultry Germplasm Determination Center) ;
  • Fu Wang (Guizhou Provincial Breeding Livestock and Poultry Germplasm Determination Center) ;
  • Zhengang Guo (Guizhou Provincial Breeding Livestock and Poultry Germplasm Determination Center) ;
  • Yongju Zhao (College of Animal Science and Technology, Southwest University) ;
  • Jipan Zhang (College of Animal Science and Technology, Southwest University)
  • 투고 : 2025.05.13
  • 심사 : 2025.08.24
  • 발행 : 2026.01.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Objective: Body conformation traits directly impact carcass performance in the meat goat industry. This study explored the population genetics of two Chinese goat breeds and identified the genomic variants associated with their body conformation traits. Methods: The Guizhou black goat (GBG, n = 104) and Hezhang black goat (HBG, n = 100) underwent genotyping through whole-genome sequencing and phenotyping by measuring their body height (BH), body length (BL), chest depth (CD), chest width (CW), chest girth (CG), rump width (RW), rump height (RH), and cannon circumference (CC). Results: The relatedness analysis showed that these goats exhibited low genetic kinship-related, with the GBG and HBG being relatively independent, albeit with some genetic introgression present. The lambda values showed that the reliability of the genome-wide association studies (GWAS) model, identifying a total of 33, 1, 6, 2, 5, 10, 21, and 13 single nucleotide polymorphisms (SNPs) as significantly correlated (p<8.33e-8) with BH, BL, CD, CW, CG, RW, RH, and CC, respectively. The GWAS for BH and RH identified the greatest number of significant SNPs, with a substantial overlap among them, mainly located in four regions: chr13_63286230-69784740 (10 SNPs), chr14_60354209-60376549 (six SNPs), and chr15_65605417-73873841 (five SNPs), and chr23_42819635-43332716 (nine SNPs). Individuals with a greater number of these SNPs displayed elevated BH and RH values. Following the annotation of all significant SNPs, 102 genes within a ±100 Kb region were identified. The most significantly enriched KEGG pathway was "Olfactory transduction", while the most significantly enriched GO terms included "cellular process" and "molecular transducer activity". Conclusion: This study investigated the population genetics of two prominent Chinese goat breeds and identified several SNPs that are significantly associated with body conformation traits. These findings offer biological insights into enhancing growth performance and hold significant potential for practical application in the genomic selection of meat goats.

키워드

과제정보

This work was financially supported by the Gene Mining and Validation of Advantageous Traits in Local Goat Breeds (Guizhou Provincial Department of Agriculture and Rural Affairs) and New Strain Breeding and Demonstration of Meat-type Goats of Guizhou Province (Key Project 033[2022]).

참고문헌

  1. Naderi S, Rezaei HR, Pompanon F, et al. The goat domestication process inferred from large-scale mitochondrial DNA analysis of wild and domestic individuals. Proc Natl Acad Sci USA 2008;105:17659-64. https://doi.org/10.1073/pnas.0804782105
  2. Li MH, Li K, Zhao SH. Diversity of Chinese indigenous goat breeds: a conservation perspective: a review. Asian-Australas J Anim Sci 2004;17:726-32. https://doi.org/10.5713/ajas.2004.726
  3. Chang L, Zheng Y, Li S, et al. Identification of genomic characteristics and selective signals in Guizhou black goat. BMC Genomics 2024;25:164. https://doi.org/10.1186/s12864-023-09954-6
  4. Shrestha JNB, Fahmy MH. Breeding goats for meat production: 3. selection and breeding strategies. Small Ruminant Res 2007;67:113-25. https://doi.org/10.1016/j.smallrumres.2005.05.040
  5. Mavrogenis AP, Papachristoforou C. Genetic and phenotypic relationships between milk production and body weight in Chios sheep and Damascus goats. Livest Prod Sci 2000;67:81-7. https://doi.org/10.1016/S0301-6226(00)00187-1
  6. Mortimer SI, Hatcher S, Fogarty NM, et al. Genetic correlations between wool traits and carcass traits in Merino sheep. J Anim Sci 2017;95:2385-98. https://doi.org/10.2527/jas.2017.1385
  7. Ayele S, Urge M, Animut G, Yusuf M. Comparative slaughter performance and carcass quality of three Ethiopian fat-tailed hair sheep breeds supplemented with two levels of concentrate. Trop Anim Health Prod 2019;51:187-98. https://doi.org/10.1007/s11250-018-1675-7
  8. Ncube KT, Dzomba EF, Hadebe K, et al. Carcass quality profiles and associated genomic regions of South African goat populations investigated using goat SNP50K genotypes. Animals 2022;12:364. https://doi.org/10.3390/ani12030364
  9. Silva Figueiredo Filho LA, Oliveira Do Ó A, Rocha Sarmento JL, Da Silva Santos NP, Torres TS. Genetic parameters for carcass traits and body size in sheep for meat production. Trop Anim Health Prod 2016;48:215-8. https://doi.org/10.1007/s11250-015-0921-5
  10. Melo TP, Fortes MRS, Bresolin T, Mota LFM, Albuquerque LG, Carvalheiro R. Multitrait meta-analysis identified genomic regions associated with sexual precocity in tropical beef cattle. J Anim Sci 2018;96:4087-99. https://doi.org/10.1093/jas/sky289
  11. McCarthy MI, Abecasis GR, Cardon LR, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 2008;9:356-69. https://doi.org/10.1038/nrg2344
  12. Hirschhorn JN, Daly MJ. Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 2005;6:95-108. https://doi.org/10.1038/nrg1521
  13. Sutera AM, Di Gerlando R, Mastrangelo S, et al. Genomewide association study for milk production traits in an economically important local dairy sheep breed. Ital J Anim Sci 2021;20:1500-5. https://doi.org/10.1080/1828051X.2021.1963865
  14. Tilahun Y, Gipson TA, Alexander T, McCallum ML, Hoyt PR. Genome-wide association study towards genomic predictive power for high production and quality of milk in American alpine goats. Int J Genomics 2020;2020:6035694. https://doi.org/10.1155/2020/6035694
  15. Zhang J, Fang J, Zhang S, Xu J, Zhao Y. Several variants on chromosome 10 are associated with coarse hair diameter in Dazu black goats (Capra hircus). Anim Genet 2025;56:e13509. https://doi.org/10.1111/age.13509
  16. Zhang JP, Xiao M, Fang JB, Huang DL, Zhao YJ. Phenotypic, transcriptomic, and genomic analyses reveal the spatiotemporal patterns and associated genes of coarse hair density in goats. Zool Res 2025;46:825-40. https://doi.org/10.24272/j.issn.2095-8137.2025.034
  17. Salgado Pardo JI, Delgado Bermejo JV, González Ariza A, et al. Candidate genes and their expressions involved in the regulation of milk and meat production and quality in goats (Capra hircus). Animals 2022;12:988. https://doi.org/10.3390/ani12080988
  18. Liu M, Woodward-Greene J, Kang X, et al. Genome-wide CNV analysis revealed variants associated with growth traits in African indigenous goats. Genomics 2020;112:1477-80. https://doi.org/10.1016/j.ygeno.2019.08.018
  19. Moaeen-ud-Din M, Muner RD, Khan MS. Genome wide association study identifies novel candidate genes for growth and body conformation traits in goats. Sci Rep 2022;12:9891. https://doi.org/10.1038/s41598-022-14018-y
  20. Gu B, Sun R, Fang X, et al. Genome-wide association study of body conformation traits by whole genome sequencing in Dazu black goats. Animals 2022;12:548. https://doi.org/10.3390/ani12050548
  21. Yang R, Zhou D, Tan X, et al. Genome-wide association study of body conformation traits in Tashi goats (Capra hircus). Animals 2024;14:1145. https://doi.org/10.3390/ani14081145
  22. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018;34:i884-90. https://doi.org/10.1093/bioinformatics/bty560
  23. Freed D, Aldana R, Weber JA, Edwards JS. The Sentieon Genomics tools: a fast and accurate solution to variant calling from next-generation sequence data. BioRxiv 115717 [Preprint]. 2017 [cited 2025 Jan 12]. https://doi.org/10.1101/115717
  24. Bickhart DM, Rosen BD, Koren S, et al. Single-molecule sequencing and chromatin conformation capture enable de novo reference assembly of the domestic goat genome. Nat Genet 2017;49:643-50. https://doi.org/10.1038/ng.3802
  25. Li H, Durbin R. Fast and accurate short read alignment with burrows–wheeler transform. Bioinformatics 2009;25:1754-60. https://doi.org/10.1093/bioinformatics/btp324
  26. McKenna A, Hanna M, Banks E, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 2010;20:1297-303. https://doi.org/10.1101/gr.107524.110
  27. Chang CC, Chow CC, Tellier LCAM, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 2015;4:1-16. https://doi.org/10.1186/s13742-015-0047-8
  28. Patterson N, Price AL, Reich D. Population structure and eigenanalysis. PLoS Genet 2006;2:2074-93. https://doi.org/10.1371/journal.pgen.0020190
  29. Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet 2012;44:821-4. https://doi.org/10.1038/ng.2310
  30. Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res 2009;19:1655-64. https://doi.org/10.1101/gr.094052.109
  31. Behr AA, Liu KZ, Liu-Fang G, Nakka P, Ramachandran S. pong: fast analysis and visualization of latent clusters in population genetic data. Bioinformatics 2016;32:2817-23. https://doi.org/10.1093/bioinformatics/btw327
  32. Yin L, Zhang H, Tang Z, et al. rMVP: a memory-efficient, visualization-enhanced, and parallel-accelerated tool for genome-wide association study. Genomics Proteomics Bioinf 2021;19:619-28. https://doi.org/10.1016/j.gpb.2020.10.007
  33. Turner SD. qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. J Open Source Softw 2018;3:731. https://doi.org/10.21105/joss.00731
  34. Smedley D, Haider S, Durinck S, et al. The BioMart community portal: an innovative alternative to large, centralized data repositories. Nucleic Acids Res 2015;43:W589-98. https://doi.org/10.1093/nar/gkv350
  35. Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 2005;21:263-5. https://doi.org/10.1093/bioinformatics/bth457
  36. Zhao Y, Han Y, Yang Y, Yuan C, Long Y, Xiao W. Genetic characterization and selection of litter size traits of Guizhou black goat and Meigu goat. PLOS ONE 2024;19:e0313297. https://doi.org/10.1371/journal.pone.0313297
  37. Li Z, Zhou B, Zhou B, et al. Effect of genetic variation in ACADM on slaughter and meat quality traits in Guizhou black goat. Small Rumin Res 2024;240:107376. https://doi.org/10.1016/j.smallrumres.2024.107376
  38. Zhou D, Long Q, Yang R, et al. Screening of candidate genes and tissue expression analysis of lambing traits for the Guizhou black goat. Pak J Zool 2023;55:2761-71. https://doi.org/10.17582/journal.pjz/20211218031227
  39. Li Y, Shen X. Effects of cadmium on liver function and its metabolomics profile in the Guizhou black goat. Metabolites 2023;13:268. https://doi.org/10.3390/metabo13020268
  40. Li Y, Shen X. Cadmium exposure affects serum metabolites and proteins in the male Guizhou black goat. Animals 2023; 13:2705. https://doi.org/10.3390/ani13172705
  41. Tam V, Patel N, Turcotte M, et al. Benefits and limitations of genome-wide association studies. Nat Rev Genet 2019;20: 467-84. https://doi.org/10.1038/s41576-019-0127-1
  42. Ren J, Gao Z, Lu Y, et al. Application of GWAS and mGWAS in livestock and poultry breeding. Animals 2024;14:2382. https://doi.org/10.3390/ani14162382
  43. Easa AA, Selionova M, Aibazov M, et al. Identification of genomic regions and candidate genes associated with body weight and body conformation traits in Karachai goats. Genes 2022;13:1773. https://doi.org/10.3390/genes13101773
  44. Han M, Wang X, Du H, et al. Genome-wide association study identifies candidate genes affecting body conformation traits of Zhongwei goat. BMC Genomics 2025;26:37. https://doi.org/10.1186/s12864-024-11097-1
  45. Liu D, Li X, Wang L, et al. Genome-wide association studies of body size traits in Tibetan sheep. BMC Genomics 2024;25:739. https://doi.org/10.1186/s12864-024-10633-3
  46. Tao L, He XY, Pan LX, et al. Genome-wide association study of body weight and conformation traits in neonatal sheep. Anim Genet 2020;51:336-40. https://doi.org/10.1111/age.12904
  47. Jiang J, Cao Y, Shan H, Wu J, Song X, Jiang Y. The GWAS analysis of body size and population verification of related SNPs in Hu sheep. Front Genet 2021;12:642552. https://doi.org/10.3389/fgene.2021.642552
  48. Deng S, Qiu Y, Zhuang Z, et al. Genome-wide association study of body conformation traits in a three-way crossbred commercial pig population. Animals 2023;13:2414. https://doi.org/10.3390/ani13152414
  49. Zhang H, Zhuang Z, Yang M, et al. Genome-wide detection of genetic loci and candidate genes for body conformation traits in Duroc × Landrace × Yorkshire crossbred pigs. Front Genet 2021;12:664343. https://doi.org/10.3389/fgene.2021.664343
  50. Liu H, Song H, Jiang Y, et al. A single-step genome wide association study on body size traits using imputation-based whole-genome sequence data in Yorkshire pigs. Front Genet 2021;12:629049. https://doi.org/10.3389/fgene.2021.629049
  51. Wu X, Fang M, Liu L, et al. Genome wide association studies for body conformation traits in the Chinese Holstein cattle population. BMC Genomics 2013;14:897. https://doi.org/10.1186/1471-2164-14-897
  52. Hu ZL, Park CA, Reecy JM. Bringing the animal QTLdb and CorrDB into the future: meeting new challenges and providing updated services. Nucleic Acids Res 2022;50:D956-61. https://doi.org/10.1093/nar/gkab1116