Definition and analysis of the panel of SNPs to be used in paternity tests for three breeds of cattle

Authors

  • Joel Domínguez-Viveros Universidad Autónoma de Chihuahua. Facultad de Zootecnia y Ecología. Periférico Francisco R. Almada km 1. 31453, Chihuahua, Chih. México. http://orcid.org/0000-0002-4011-6142
  • Adán Medellín-Cazares Universidad Autónoma de Chihuahua. Facultad de Zootecnia y Ecología. Periférico Francisco R. Almada km 1. 31453, Chihuahua, Chih. México.
  • Nelson Aguilar-Palma Universidad Autónoma de Chihuahua. Facultad de Zootecnia y Ecología. Periférico Francisco R. Almada km 1. 31453, Chihuahua, Chih. México. http://orcid.org/0000-0002-1654-8930
  • Francisco Joel Jahuey-Martínez Universidad Autónoma de Chihuahua. Facultad de Zootecnia y Ecología. Periférico Francisco R. Almada km 1. 31453, Chihuahua, Chih. México. http://orcid.org/0000-0002-6562-5875
  • Felipe Alonso Rodríguez-Almeida Universidad Autónoma de Chihuahua. Facultad de Zootecnia y Ecología. Periférico Francisco R. Almada km 1. 31453, Chihuahua, Chih. México.

DOI:

https://doi.org/10.22319/rmcp.v12i3.5771

Keywords:

Heterozygosity, Exclusion probability, Identity probability, Polymorphism, Shannon Index

Abstract

In order to define the SNP panel for paternity tests in cattle, genotypes were analyzed in three breeds (number of SNPs evaluated and individuals sampled): Hereford (HER; 202; 1317), Brangus (BRA; 217; 3431) and Limousin (LIM; 151; 8205). Within breed, SNPs with a percentage of genotyped individuals (PGI) less than 90 %, with Hardy-Weinberg disequilibrium (HW; P<0.05), with allele frequency less than 0.10 or less and with linkage disequilibrium, where the correlation between genotypic frequencies was greater than 0.25, were discarded. The levels of expected (He) and observed (Ho) heterozygosity, polymorphic information content (PIC) were estimated; as well as the Shannon index, the fixation index and effective population size (Ne). The combined exclusion probability (CEP) and identity probability (CIP) were calculated. The final panel was 121, 188 and 113 SNPs in HER, BRA and LIM, respectively; the main source of discard was HW followed by PGI. Levels of Ho and He were above 0.40; CIP was greater than 0.32 and Ne presented estimates above 181.3. The results for CEP were higher than 0.999999; for CIP, they were below 1 x 10-20.

Downloads

Download data is not yet available.

References

CONARGEN. Guía técnica de programas de control de producción y mejoramiento genético en bovinos. Consejo Nacional de los Recursos Genéticos Pecuarios. México. 2010.

Banos G, Wiggans GR, Powell RL. Impact of paternity errors in cow’s identification on genetic evaluations and international comparisons. J Dairy Sci 2001;84:2523-2529.

Atkin FC, Dieters MJ, Stringer JK. Impact of depth of pedigree and inclusion of historical data on the estimation of additive variance and breeding values in a sugarcane breeding program. Theo Appl Gen 2009;119:555-565.

Ramírez-Valverde R, Delgadillo-Zapata AR, Domínguez-Viveros J, Hidalgo-Moreno JA, Núñez-Domínguez R, Rodríguez-Almeida FA, et al. Análisis del pedigrí en la determinación de la diversidad genética de poblaciones bovinas para carne mexicana. Rev Mex Cienc Pecu 2018;9:614-635.

Visscher PM, Woolliams JA, Smith D, Williams JL. Estimation of pedigree errors in the UK dairy population using microsatellite markers and the impact on selection. J Dairy Sci 2002;85:2368-2375.

Sanders K, Bennewitz J, Kalm E. Wrong and missing sire information affects genetic gain in the Angeln dairy cattle population. J Dairy Sci 2006;89:315-321.

Parlato E, Van Vleck LD. Effect of parentage misidentification on estimates of genetic parameters for milk yield in the Mediterranean Italian buffalo population. J Dairy Sci 2012;95:4059-4064.

Raoul J, Palhiere I, Astruc JM, Elsen JM. Genetic and economic effects of the increase in female paternal filiations by parentage assignment in sheep and goat breeding programs. J Anim Sci 2016;94:3663–3683.

Vignal A, Milan D, SanCristobal M, Eggen A. A review on SNP and other types of molecular markers and their use in animal genetics. Genet Sel Evol 2002;34:275-305.

Cañón J. Using molecular information in animal improvement programs. Rev Corpoica 2006;7:5-15.

Dekkers, JCM. Application of genomics tools to animal breeding. Current Genomics 2012;13:207-212.

Flanagan SP, Jones AG. The future of parentage analysis: from microsatellites to SNPs and beyond. Mol Ecol 2019;28:544-567.

Morrin R, Boscher M. Cattle molecular markers and parentage testing workshop. ISAG Conference 2012;1-7.

Strucken EM, Lee SH, Lee HK, Song KD, Gibson JP, Gondro C. How many markers are enough? Factors influencing parentage testing in different livestock populations. J Anim Breed Genet 2016;133:13-23.

Baruch E, Weller J. Estimation of the number of SNP genetic markers required for parentage verification. Anim Genet 2008;39:474-479.

Panetto JCD, Machado MA, da Silva MVG, Barbosa RS, dos Santos GG, Leite RMHR, Peixoto MGC. Parentage assignment using SNP markers, inbreeding and population size for the Brazilian Red Sindhi cattle. Livest Sci 2017;204:33-38.

Fernández ME, Goszczynski DE, Liron JP, Villegas-Castagnasso EE, Cariño MH, Ripoli MV, et al. Comparison of the effectiveness of microsatellites and SNP panels for genetic identification, traceability, and assessment parentage in an inbred Angus herd. Genet Mol Biol 2013;36:185-191.

Zhang T, Guo L, Shi M, Xu L, Chen Y, Zhang L, Gao H, Li J, Gao X. Selection and effectiveness of informative SNPs for paternity in Chinese Simmental cattle based on a high-density SNP array. Gene 2018;673:211-216.

Hu L, Li D, Chu Q, Wang Y, Zhou L, Yu Y, Zhang Y, et al. Selection and implementation of SNP markers for parentage analysis in a Chinese crossbred cattle population. Res Square 2020;e30446/v1.

Heaton MP, Harhay GP, Bennett GL, Stone RT, Grosse WM, Casas E, et al. Selection and use of SNP markers for animal identification and paternity analysis in U.S. beef cattle. Mamm Gen 2002;13:272-281.

Van Eenennaam AL, Weaber RL, Draker DJ, Penedo MCT, Quaas RL, Garrick DJ, Pollak EJ. DNA-based paternity analysis and genetic evaluation in a large commercial cattle ranch setting. J Anim Sci 2007;85:3159-3169.

Honda T, Katsuta T, Mukai F. Simulation study on parentage analysis with SNPs in the Japanese cattle population. Asian-Aust J Anim Sci 2009;10:1351-1358.

Strucken EM, Gudex B, Ferdosi MH, Lee HK, Song KD, Gibson JP, et al. Performance of different SNP panels for parentage testing in two East Asian cattle breeds. Anim Genet 2014;45:572-575.

Werner FAO, Durstewitz G, Habemann FA, Thaller G, Kramer W, Kollers S, et al. Detection, and characterization of SNP useful for identity control and parentage testing in major European dairy breeds. Anim Genet 2004;35:44-49.

Negrini R, Nicoloso L, Crepaldi P, Milanesi E, Colli L, Chegdani F, et al. Assessing SNP markers for assigning individuals to cattle populations. Anim Genet 2009;40:18-26.

Allen AR, Taylor M, McKeown B, Curry AI, Lavery JF, Mitchell A, et al. Compilation of a panel of informative single nucleotide polymorphisms for bovine identification in the Northern Iris cattle population. BMC Genet 2010;11:Art 5.

Waples RS. A bias correction for estimate of effective population size base on linkage disequilibrium at unlinked loci. Conserv Genet 2006;7:167-184.

Jamieson A, Taylor SC. Comparisons of three probability formulae for parentage exclusion. Anim Genet 1997;28:397-400.

Hara K, Watabe H, Sasazaki S, Mukai F, Mannen H. Development of SNP markers for individual identification and parentage test in Japanese black cattle population. Anim Sci J 2010;81:152-157.

Olenski K, Kaminski S, Tokarska M, Hering DM. Subset of SNPs for parental identification in European bison Lowland-Bialowieza line (Bison bonasus bonasus). Conserv Genet Res 2018;10:73-78.

Waits L, Luikart G, Taberlet P. Estimating the probability of identity among genotypes in natural populations cautions and guidelines. Mol Ecol 2001;10:249-256.

Goudet J. FSTAT: A computer program to calculate F-Statistics. J Heredity 1995;86:485-486.

Waples RS, Do Chi. LDNE: a program for estimating effective population size from data on linkage disequilibrium. Mol Ecol 2008;8:753-756.

Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research – an update. Bioinformatics 2012;28:2537-2539.

Published

2021-12-15

How to Cite

Domínguez-Viveros, J., Medellín-Cazares, A., Aguilar-Palma, N., Jahuey-Martínez, F. J., & Rodríguez-Almeida, F. A. (2021). Definition and analysis of the panel of SNPs to be used in paternity tests for three breeds of cattle. Revista Mexicana De Ciencias Pecuarias, 12(3), 987–995. https://doi.org/10.22319/rmcp.v12i3.5771
Metrics
Views/Downloads
  • Abstract
    977
  • PDF (Español)
    356
  • PDF
    211

Issue

Section

Research Notes

Metrics

Similar Articles

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 > >> 

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)

1 2 > >>