Journal of Animal Production, Print ISSN: 2008-6776., Online ISSN: 2382-994X

Investigating the performance of different methods in the selection of causative SNP markers on the breed differentiation of horses

Document Type : Research Paper

Authors

Siavash Manzoori 1
Amir Hossein Khaltabadi Farahani 2
Mohammad Hossein Moradi 3

1 Ph.D. Student of animal genetic and breeding, Department of Animal Science, Faculty of Agriculture and Natural Resources, Tarbiat Modares University, Tehran, Iran.

2 Associate Professor, Department of Animal Science, Faculty of Agriculture and Natural Resources, Arak University, Arak, 38156-8-8349, Iran

3 Associate Professor, Department of Animal Science, Faculty of Agriculture and Natural Resources, Arak University, Arak. Iran.

https://doi.org/10.22059/jap.2023.347298.623703
Abstract
The present study was conducted in order to select effective markers in breed discrimination and compare the performance of SNP marker selection methods with the data of 304 animals from 14 different breeds that were genotyped using the Illumina SNP50K marker panel. Knowledge of genetic structure are very important for better understanding of genetic changes in genomic studies. The information content of each marker is used as an index for selecting markers in reducing the size of marker panels. To estimate the information content of each marker, the following selection methods were used: Fst (pairwise & global), Theta, Delta, D, Gst, G'st, G"st and Principal Component Analysis. In this study, the logarithm of the likelihood ratio was used to select markers. According to the results, all selection methods for identifying markers had similar performance. The number of common markers between the methods was at least 42 markers and at most 499 SNP markers. In general, the F_ST statistical method required a smaller number of markers to achieve a successful assignment. G'st and G"st statistics showed poor performance with more than 350 markers to achieve 95% correct assignment. It should be noted that with only the top 60 selected markers, it is possible to achieve a success rate of more than 70%. According to the results, Wright's paired Fst had better performance than other SNP selection methods. The obtained results lead to the creation of exclusive panels to identify various breeds, which have great economic importance.

Keywords

Comparison
Discrimination
Genetic structure
Genome
Single nucleotide polymorphism
  1. Makina SO, Muchadeyi FC, Van Marle-Köster E, Mac-Neil MD and Maiwashe A (2014) Genetic diversity and population structure among six cattle breeds in South Africa using a whole genome SNP panel. Frontiers in Genetics, 5(333) doi:10.3389/fgene.2014.00333.
  2. Rannala B and Mountain JL (1997) Detecting immigration by using multilocus genotypes. Proceedings of the National Academy of Sciences of the United States of America, 94(17): 9197-9201.
  3. Weller JI, Seroussi E and Ron M (2006) Estimation of the number of genetic markers required for individual animal identification accounting for genotyping errors. Animal Genetics, 37(4): 387-389. doi:10.1111/j.1365-2052.2006.01455.x.
  4. Wright S (1951) The genetical structure of populations. Annals of Eugenics, 15(1):323-354. doi:10.1111/j.1469-1809.1949.tb02451.x.
  5. Holsinger KE and Weir BS (2009) Genetics in geographically structured populations: Defining, estimating and interpreting F(ST) Nature Reviews Genetics, 10(9): 639-650, doi:10.1038/nrg2611.
  6. Weir BS and Cockerham CC (1984) Estimating F-Statistics for the Analysis of Population Structure. Evolution, 38(6): 1358-1370. doi:10.2307/2408641.
  7. Akey JM, Zhang G, Zhang K, Jin L and Shriver MD (2002). Interrogating a high-density SNP map for signatures of natural selection. Genome Research, 12, doi:10.1101/gr.631202.
  8. Kijas JW, Townley D, Dalrymple BP, Heaton MP, Maddox JF, McGrath A and et al. (2009) A Genome Wide Survey of SNP Variation Reveals the Genetic Structure of Sheep Breeds. PLOS ONE, 4, doi:10.1371/journal.pone.0004668.
  9. Jost LOU (2008) GST and its relatives do not measure differentiation. Molecular Ecology, 17(18): 4015-4026. doi:10.1111/j.1365-294X.2008.03887.x.
  10. Meirmans PG and Hedrick PW (2011) Assessing population structure: FST and related measures. Molecular Ecology Resources, 11(1): 5-18. doi:10.1111/j.1755-0998.2010.02927.x.
  11. Nei M and Chesser RK (1983) Estimation of fixation indices and gene diversities. Annals of Human Genetics, 47(3): 253-259. doi:10.1111/j.1469-1809.1983.tb00993.x.
  12. Hedrick PW (2005) A Standardized genetic differentiation measure. Evolution, 59(8):1633-1638. doi:10.1111/j.0014-3820.2005.tb01814.x.
  13. Smith MW, Lautenberger JA, Shin HD, Chretien JP, Shrestha S, Gilbert DA et al. (2001) Markers for mapping by admixture linkage disequilibrium in African American and Hispanic populations. American Journal of Human Genetics, 69. doi:10.1086/323922.
  14. Paschou P, Ziv E, Burchard EG, Choudhry S, Rodriguez-Cintron W, Mahoney MW et al. (2007) PCA-Correlated SNPs for Structure Identification in Worldwide Human Populations. PLoS Genetics, 3(9):e160, doi:10.1371/journal.pgen.0030160.
  15. Petersen, J. L, Mickelson, J. R, Cothran, E. G, Andersson, L. S, Axelsson, J, Bailey, E, et al. (2013). Genetic Diversity in the Modern Horse Illustrated from Genome-Wide SNP Data. PLOS ONE, 8(1):e54997. doi:10.1371/journal.pone.0054997.
  16. R Core Team (2017) R: A Language and Environment for Statistical Computing. Retrieved from https://www.R-project.org/
  17. Paetkau D, Calvert W, Stirling, I and Strobeck C (1995) Microsatellite analysis of population structure in Canadian polar bears. Molecular Ecology, 4(3): 347-354.
  18. Rosenberg NA, Li LM, Ward R and Pritchard JK (2003) Informativeness of genetic markers for inference of ancestry. American Journal of Human Genetics, 73, doi:10.1086/380416.
  19. Lao O, van Duijn K, Kersbergen P, de Knijff P and Kayser M (2006) Proportioning whole-genome single-nucleotide-polymorphism diversity for the identification of geographic population structure and genetic ancestry, American Journal of Human Genetics, 78, doi:10.1086/501531.
  20. Liu N, Chen L, Wang S, Oh C and Zhao H (2005) Comparison of single-nucleotide polymorphisms and microsatellites in inference of population structure, BMC Genetics, Dec 30;6, doi: 10.1186/1471-2156-6-S1-S26. PMID: 16451635; PMCID: PMC1866760.
  21. Smouse PE, Spielman RS and Park MH (1982) Multiple-locus allocation of individuals to groups as a function of the genetic variatio within and differences among human populations, American Naturalist, 119, doi:10.1086/283925.
  22. Falush D, Stephens M and Pritchard JK (2003) Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies, Genetics, 164(4): 1567-1587.
  23. Maudet C, Luikart G and Taberlet P (2002) Genetic diversity and assignment tests among seven French cattle breeds based on microsatellite DNA analysis, Journal of Animal Science, 80(4): 942- https://doi.org/10.2527/2002.804942x.
  24. Negrini R, Nicoloso L, Crepaldi P, Milanesi E, Colli L, Chegdani F and et al. (2009) Assessing SNP markers for assigning individuals to cattle populations, Animal genetics, 40(1): 18-26, doi: 10.1111/j.1365-2052.2008.01800.x.

 

Journal of Animal Production, Print ISSN: 2008-6776., Online ISSN: 2382-994X
Volume 25, Issue 1
Spring 2023
Pages 1-11

Home

Guide for Authors

Submit Manuscript

Reviewers

Contact Us