Animal Bioscience

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

DOI QR Code

Monitoring changes in the genetic structure of Brown Tsaiya duck selected for feeding efficiency by microsatellite markers

  • Yi-Ying Chang (ILan Branch, Livestock Research Institute, Council of Agriculture) ;
  • Hsiu-Chou Liu (ILan Branch, Livestock Research Institute, Council of Agriculture) ;
  • Chih-Feng Chen (Department of Animal Science, National Chung Hsing University)
  • Received : 2022.05.27
  • Accepted : 2022.09.12
  • Published : 2023.03.01
Download PDF
⟨ Previous Next ⟩

Abstract

Objective: Few studies have genetically monitored chickens over time, and no research has been conducted on ducks. To ensure the sustainable management of key duck breeds, we used microsatellite markers to monitor Brown Tsaiya ducks over time genetically. Methods: The second, fourth, sixth to eighth generations of the Brown Tsaiya duck selected for feeding efficiency and control lines were included in this study to investigate the genetic variations, effective population size, population structure and the differentiation between populations over time with 11 microsatellite markers derived from Brown Tsaiya duck. Results: The results showed there were a slight decrease in the genetic variations and an increase in within-population inbreeding coefficient (FIS) in both lines, but no consistent increase in FIS was observed in each line. The effective population size in the second and eighth generations was 27.2 for the selected line and 23.9 for the control line. The change in allele richness showed a downward trend over time, and the selected line was slightly lower than the control line in each generation. The number of private alleles (Np) in the selected line were higher than in the control line. Moderate differentiation was observed between the second and eighth generations in the selected line (FST = 0.0510) and the control line (FST = 0.0606). Overall, differentiation tended to increase with each generation, but genetic variation and structure did not change considerably after six generations in the two lines. Conclusion: This study provides a reference for poultry conservation and helps to implement cross-generation genetic monitoring and breeding plans in other duck breeds or lines to promote sustainable management.

Keywords

Acknowledgement

We would like to thank the staff members of the Ilan Branch of the Livestock Research Institute of the Council of Agriculture for their assistance in the feeding and care of the animals. We also would like to acknowledge the National Center for Genome Medicine of the National Science Council of Taiwan for their technical support with the microsatellite genotyping.

References

  1. Liu HC, Tu TC, Marie-Etancelin C, Lee YP, Huang JF, CF Chen. Genetic parameters of residual feed intake in the Brown Tsaiya duck. Taiwan Lives Res 2012;45:131-40.
  2. FAO. Phenotypic characterization of animal genetic resources. FAO Animal Production and Health Guidelines No. 11. Rome, Italy: FAO; 2012.
  3. Magpantay VA, Lambio AL, Laude RP, et al. Genetic diversity of Philippine mallard duck (Anas platyrhynchos domesticus L.) based on SSR markers. Philipp J Sci 2019;148:725-33.
  4. Pham LD, Do DN, Nam LQ, et al. Evaluation of genetic diversity and population structure in four indigenous duck breeds in Vietnam. Anim Biotechnol 2022;33:1065-72. https://doi.org/10.1080/10495398.2020.1868485
  5. Veeramani P, Prabakaran R, Sivaselvam SN, et al. Genetic diversity of six duck populations in India. Indian J Anim Res 2021;B4379. https://doi.org/10.18805/IJAR.B-4379
  6. Zhang X, He Y, Zhang W, et al. Development of microsatellite marker system to determine the genetic diversity of experimental chicken, duck, goose, and pigeon populations. Biomed Res Int 2021;2021:8851888. http://doi.org/10.1155/2021/8851888
  7. Zhang Y, Wang L, Bian Y, et al. Marginal diversity analysis of conservation of Chinese domestic duck breeds. Sci Rep 2019;9:13141. https://doi.org/10.1038/s41598-019-49652-6
  8. Gemayel R, Vinces MD, Legendre M, et al. Variable tandem repeats accelerate evolution of coding and regulatory sequences. Annu Rev Genet 2010;44:445-77. https://doi.org/10.1146/annurev-genet-072610-155046
  9. Liu ZJ, Cordes JF. DNA marker technologies and their applications in aquaculture genetics. Aquaculture 2004;238:1-37. https://doi.org/10.1016/j.aquaculture.2004.05.027
  10. Tamaki K. Minisatellite and microsatellite DNA typing analysis. In: Rapley R, Whitehouse D, editors. Molecular forensics. Chichester, England: John Wiley & Sons, Ltd; 2007. pp. 71-86.
  11. Palinkas-Bodzsar N, Sztan N, Molnar T, Hidas A. Gene conservation of six Hungarian local chicken breeds maintained in small populations over time. PloS one 2020;15:e0238849. https://doi.org/10.1371/journal.pone.0238849
  12. Tu Y, Shu J, Ji G, Zhang M, Zou J. Monitoring conservation effects on a Chinese indigenous chicken breed using major histocompatibility complex B-G gene and DNA Barcodes. Asian-Australas J Anim Sci 2018;31:1558-64. https://doi.org/10.5713/ajas.17.0627
  13. Chang YY, Chang WP, Wei LY, et al. Study on genetic structure in different Brown Tsaiya duck lines with microsatellite markers. J Chin Soc Anim Sci 2018;47:221-38.
  14. Lai FY, Chang YY, Chen YC, et al. Monitoring of genetically close Tsaiya duck populations using novel microsatellite markers with high polymorphism. Asian-Australas J Anim Sci 2020;33:888-901. https://doi.org/10.5713/ajas.19.0175
  15. Bush KL, Vinsky MD, Aldridge CL, Paszkowski CA. A comparison of sample types varying in invasiveness for use in DNA sex determination in an endangered population of greater Sage-Grouse (Centrocercus uropihasianus). Conserv Genet 2005;6:867-70. https://doi.org/10.1007/s10592-005-9040-6
  16. Hsiao MC, Liu HC, Hsu YC, et al. Isolation and characterization of microsatellite markers in Tsaiya duck. AsianAustralas J Anim Sci 2008;21:624-7. https://doi.org/10.5713/ajas.2008.70366
  17. Nei M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 1978;89:583-90. https://doi.org/10.1093/genetics/89.3.583
  18. 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.
  19. Kalinowski ST, Taper ML, Marshall TC. Revising how the computer program cervus accommodates genotyping error increases success in paternity assignment. Mol Ecol 2007;16:1099-106. https://doi.org/10.1111/j.1365-294X.2007.03089.x
  20. Yeh FC, Boyle TBJ. Population genetic analysis of co-dominant and dominant markers and quantitative traits. Belg J Bot 1997;129:157.
  21. Guo SW, Thompson EA. Performing the exact test of HardyWeinberg proportion for multiple alleles. Biometrics 1992;48: 361-72. https://doi.org/10.2307/2532296
  22. Manly BFJ. The statistics of natural selection on animal populations. London, UK: Chapman and Hall; 1985. pp. 272-82.
  23. Chapuis MP, Estoup A. Microsatellite null alleles and estimation of population differentiation. Mol Biol Evol 2007;24:621-31. https://doi.org/10.1093/molbev/msl191
  24. Do C, Waples RS, Peel D, et al. NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol Ecol Resour 2014;14:209-14. https://doi.org/10.1111/1755-0998.12157
  25. Jorde PE, Ryman N. Unbiased estimator for genetic drift and effective population size. Genetics 2007;177:927-35. https://doi.org/10.1534/genetics.107.075481
  26. Goudet J. FSTAT a program to estimate and test gene diversities and fixation indices (version 293) [Internet] c2001 [cited 2022 Aug 10]. Available from: https://www2.unil.ch/popgen/softwares/fstat.htm
  27. 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
  28. Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research--an update. Bioinformatics 2012;28:2537-9. https://doi.org/10.1093/bioinformatics/bts460
  29. Jombart T, Ahmed I. Adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics 2011;27:3070-1. https://doi.org/10.1093/bioinformatics/btr521
  30. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics 2000;155: 945-59. https://doi.org/10.1093/genetics/155.2.945
  31. Earl DA, vonHoldt BM. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 2012;4:359-61. https://doi.org/10.1007/s12686-011-9548-7
  32. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 2005;14:2611-20. https://doi.org/10.1111/j.1365-294X.2005.02553.x
  33. Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I. Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol Ecol Resour 2015;15:1179-91. https://doi.org/10.1111/1755-0998.12387
  34. Sultana H, Seo D, Choi N, et al. Genetic diversity analyses of Asian duck populations using 24 microsatellite markers. Korean J Poult 2017;44:75-81. https://doi.org/10.5536/KJPS.2017.44.2.75
  35. Chang YY, Wei LY, Chen JY, Liu HC. Study on genetic structure and phylogeny in Chihsin duck selected for the duration of fertility. Taiwan Lives Res 2022 (accepted).
  36. Pampoulie C, Danielsdottir AK, Storr-Paulsen M, Hovgard H, Hjorleifsson E, Steinarsson B. Neutral and nonneutral genetic markers revealed the presence of inshore and offshore stock components of Atlantic cod in greenland waters. Trans Am Fish Soc 2011;140:307-19. https://doi.org/10.1080/00028487.2011.567850
  37. Zimmerman SJ, Aldridge CL, Oyler-McCance SJ. An empirical comparison of population genetic analyses using microsatellite and SNP data for a species of conservation concern. BMC Genomics 2020;21:382. https://doi.org/10.1186/s12864-020-06783-9
  38. Hale ML, Burg TM, Steeves TE. Sampling for microsatellitebased population genetic studies: 25 to 30 individuals per population is enough to accurately estimate allele frequencies. PloS one 2012;7:e45170. https://doi.org/10.1371/journal.pone.0045170
  39. Dharmarajan G, Beatty WS, Rhodes Jr. OE. Heterozygote deficiencies caused by a Wahlund effect: Dispelling unfounded expectations. J Wildl Manage 2013;77:226-34. https://doi.org/10.1002/jwmg.458
  40. Zanetti E, De Marchi M, Abbadi M, Cassandro M. Variation of genetic diversity over time in local Italian chicken breeds undergoing in situ conservation. Poult Sci 2011;90:2195-201. https://doi.org/10.3382/ps.2011-01527
  41. Petit RJ, El Mousadik A, Pons O. Identifying populations for conservation on the basis of genetic markers. Conserv Biol 1998;12:844-55. https://doi.org/10.1111/j.1523-1739.1998.96489.x
  42. Manjula P, Kim M, Cho S, Seo D, Lee JH. Highlevels of genetic variation in MHC-linked microsatellite markers from native chicken breeds. Genes 2021;12:240. https://doi.org/10.3390/genes12020240
  43. Zhang M, Han W, Tang H, et al. Genomic diversity dynamics in conserved chicken populations are revealed by genomewide SNPs. BMC Genom 2018;19:598. https://doi.org/10. 1186/s12864-018-4973-6 https://doi.org/10.1186/s12864-018-4973-6
  44. Wright S. Evolution and the genetics of populations. Chicago, IL, USA: University of Chicago Press; 1978.