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Evaluation of the potential use of a meta-population for genomic selection in autochthonous beef cattle populations

Published online by Cambridge University Press:  02 November 2017

E. F. Mouresan ,
J. J. Cañas-Álvarez ,
A. González-Rodríguez ,
S. Munilla ,
J. Altarriba ,
C. Díaz ,
J. A. Baró ,
A. Molina ,
J. Piedrafita  and
L. Varona
E. F. Mouresan
Affiliation:
Departamento de Anatomía, Embriología y Genética, Universidad de Zaragoza, 50013 Zaragoza, Spain
J. J. Cañas-Álvarez
Affiliation:
Grup de Recerca en Remugants, Departament de Ciència Animal 6i dels Aliments, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
A. González-Rodríguez
Affiliation:
Departamento de Anatomía, Embriología y Genética, Universidad de Zaragoza, 50013 Zaragoza, Spain
S. Munilla
Affiliation:
Departamento de Anatomía, Embriología y Genética, Universidad de Zaragoza, 50013 Zaragoza, Spain Departamento de Producción Animal, Facultad de Agronomía, Universidad de Buenos Aires, 1417 CABA, Argentina
J. Altarriba
Affiliation:
Departamento de Anatomía, Embriología y Genética, Universidad de Zaragoza, 50013 Zaragoza, Spain Instituto Agroalimentario de Aragón (IA2), 50013 Zaragoza, Spain
C. Díaz
Affiliation:
Departamento de Mejora Genética Animal, INIA, 28040 Madrid, Spain
J. A. Baró
Affiliation:
Departamento de Ciencias Agroforestales, Universidad de Valladolid, 34004 Palencia, Spain
A. Molina
Affiliation:
MERAGEM, Universidad de Córdoba, 14071 Córdoba, Spain
J. Piedrafita
Affiliation:
Grup de Recerca en Remugants, Departament de Ciència Animal 6i dels Aliments, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
L. Varona*
Affiliation:
Departamento de Anatomía, Embriología y Genética, Universidad de Zaragoza, 50013 Zaragoza, Spain Instituto Agroalimentario de Aragón (IA2), 50013 Zaragoza, Spain
*
†E-mail: lvarona@unizar.es
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Abstract

This study investigated the potential application of genomic selection under a multi-breed scheme in the Spanish autochthonous beef cattle populations using a simulation study that replicates the structure of linkage disequilibrium obtained from a sample of 25 triplets of sire/dam/offspring per population and using the BovineHD Beadchip. Purebred and combined reference sets were used for the genomic evaluation and several scenarios of different genetic architecture of the trait were investigated. The single-breed evaluations yielded the highest within-breed accuracies. Across breed accuracies were found low but positive on average confirming the genetic connectedness between the populations. If the same genotyping effort is split in several populations, the accuracies were lower when compared with single-breed evaluation, but showed a small advantage over small-sized purebred reference sets over the accuracies of subsequent generations. Besides, the genetic architecture of the trait did not show any relevant effect on the accuracy with the exception of rare variants, which yielded slightly lower results and higher loss of predictive ability over the generations.

Keywords

beef cattlegenomic selectionacross populationaccuracymeta-population
Type
Research Article
Information
animal , Volume 12 , Issue 7 , July 2018 , pp. 1350 - 1357
Copyright
© The Animal Consortium 2017 

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References

Beja-Pereira, A, Alexandrino, P, Bessa, I, Carretero, Y, Dunner, S, Ferrand, N, Jordana, J, Laloe, D, Moazami-Goudarzi, K, Sanchez, A and Cañon, J 2003. Genetic characterization of Southwestern European Bovine breeds: a historical and biogeographical reassessment with a set of 16 microsatellites. Journal of Heredity 94, 243–250.CrossRefGoogle Scholar
Berry, DP, Garcia, JF and Garrick, DJ 2016. Development and implementation of genomic predictions in beef cattle. Animal Frontiers 6, 32.CrossRefGoogle Scholar
Bolormaa, S, Pryce, JE, Kemper, K, Savin, K, Hayes, BJ, Barendse, W, Zhang, Y, Reich, CM, Mason, BA, Bunch, RJ, Harrison, BE, Reverter, A., Herd, RM, Tier, B, Graser, HU and Goddard, ME 2013. Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in Bos taurus, Bos indicus, and composite beef cattle. Journal of Animal Science 91, 3088–3104.CrossRefGoogle ScholarPubMed
Browning, BL and Browning, SR 2009. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. American Journal of Human Genetics 84, 210–223.Google Scholar
Cañas-Álvarez, JJ, González-Rodríguez, A, Munilla, S, Varona, L, Díaz, C, Baro, JA, Altarriba, J, Molina, A and Piedrafita, J 2015. Genetic diversity and divergence among Spanish beef cattle breeds assessed by a bovine high-density SNP chip. Journal of Animal Science 93, 5164–5174.Google Scholar
Cañas-Álvarez, JJ, Mouresan, E, Varona, L, Diaz, C, Molina, A, Baro, JA, Altarriba, J, Carabano, M, Casellas, J and Piedrafita, J 2016. Linkage disequilibrium, persistence of phase and effective population size in Spanish local beef cattle breeds assessed through a SNP high density chip. Journal of Animal Science 94, 2779–2788.Google Scholar
Chen, L, Schenkel, F, Vinsky, M, Crews, DH and Li, C 2013. Accuracy of predicting genomic breeding values for residual feed intake in Angus and Charolais beef cattle. Journal of Animal Science 91, 4669–4678.Google Scholar
Clark, SA, Hickey, JM, Daetwyler, HD and van der Werf, JH 2012. The Importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes. Genetics Selection Evolution 44, 4.CrossRefGoogle ScholarPubMed
De Roos, APW, Hayes, BJ and Goddard, ME 2009. Reliability of genomic predictions across multiple populations. Genetics 183, 1545–1553.Google Scholar
Falconer, DS and Mackay, TFC 1996. Introduction to quantitative genetics, 4th edition. Addison Wesley Longman Limited, Harlow.Google Scholar
Gibson, G 2012. Rare and common variants: twenty arguments. Nature Reviews Genetics 13, 135–145.Google Scholar
Gonzalez-Recio, O, Daetwyler, HD, MacLeod, IM, Pryce, JE, Bowman, PJ, Hayes, BJ and Goddard, ME 2015. Rare variants in transcript and potential regulatory regions explain a small percentage of the missing heritability of complex traits in cattle. PLoS One 10, e0143945.Google Scholar
Gunderson, KL, Steemers, FJ, Lee, G, Mendoza, LG and Chee, MS 2005. A genome-wide scalable SNP genotyping assay using microarray technology. Nature Genetics 37, 549–554.CrossRefGoogle ScholarPubMed
Gusev, A, Lee, SH, Trynka, G, Finucane, H, Vilhjalmsson, BJ, Xu, H, Zang, C, Ripke, S, Bulik-Sullivan, B, Stahl, E, Kahler, AK, Hultman, CM, Purcell, SM, McCarroll, SA, Daly, M, Pasaniuc, B, Sullivan, PF, Neale, BM, Wray, NR, Raychaudhuri, S and Price, AL 2014. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. American Journal of Human Genetics 95, 535–552.Google Scholar
Harris, BL, Johnson, DL and Spelman, RJ 2008. Genomic selection in New Zealand and the implications for national genetic evaluation. Proceedings of 36th ICAR Biennial Session, Niagara Falls, USA. p. 325.Google Scholar
Hayes, BJ, Bowman, PJ, Chamberlain, AJ and Goddard, ME 2009. Invited review: genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science 92, 433–443.CrossRefGoogle ScholarPubMed
Heidaritabar, M, Wolc, A, Arango, J, Zeng, J, Settar, P, Fulton, JE, O’Sullivan, NP, Bastiaansen, JW, Fernando, RL, Garrick, DJ and Dekkers, JC 2016. Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers. Journal of Animal Breeding and Genetics 133, 334–346.CrossRefGoogle ScholarPubMed
Hill, WG and Robertson, A 1968. Linkage disequilibrium in finite populations. Theoretical Applied Genetics 38, 226–231.CrossRefGoogle ScholarPubMed
Iheshiulor, OOM, Woolliams, JA, Yu, X, Wellmann, R and Meuwissen, THE 2016. Within – and across-breed genomic prediction using whole-genome sequence and single nucleotide polymorphism panels. Genetics Selection Evolution 48, 15.Google Scholar
Kachman, SD, Spanger, ML, Bennett, GL, Hanford, KJ, Kuehn, L.A, Snelling, WM, Thallman, RM, Saatchi, M, Garrick, DJ, Schnabel, RD, Taylor, JF and Pollak, EJ 2013. Comparison of molecular breeding values based on within- and across-breed training in beef cattle. Genetics Selection Evolution 45, 30.CrossRefGoogle ScholarPubMed
Karoui, S, Carabaño, MJ, Díaz, C and Legarra, A 2012. Joint genomic evaluation of French dairy cattle breeds using multiple-trait models. Genetics Selection Evolution 44, 10.CrossRefGoogle ScholarPubMed
Kizilkaya, K, Fernando, RL and Garrick, DJ 2010. Genomic prediction of simulated multibreed and purebred performance using observed fifty thousand single nucleotide polymorphism genotypes. Journal of Animal Science 88, 544–551.CrossRefGoogle ScholarPubMed
Legarra, A and Misztal, I 2008. Technical note: computing strategies in genome-wide selection. Journal of Dairy Science 91, 360–366.CrossRefGoogle ScholarPubMed
Legarra, A, Robert-Granié, C, Manfredi, E and Elsen, JM 2008. Performance of genomic selection in mice. Genetics 180, 611–618.CrossRefGoogle ScholarPubMed
Lourenco, DAL, Tsuruta, S, Fragomeni, BO, Masuda, Y, Aguilar, I, Legarra, A, Bertrand, JK, Amen, TS, Wang, L, Moser, DW and Misztal, I 2015. Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus. Journal of Animal Science 93, 2653–2662.Google Scholar
Meuwissen, THE, Hayes, BJ and Goddard, ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.Google Scholar
Purcell, S, Neale, B, Todd-Brown, K, Thomas, L, Ferreira, MAR, Bender, D, Maller, J, Sklar, P, de Bakker, PIW, Daly, MJ and Sham, PC 2007. PLINK: a tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics 81, 559–575.Google Scholar
Reiner-Benaim, A, Erza, E and Weller, JI 2017. Optimization of a genomic breeding program for a moderately sized dairy cattle population. Journal of Dairy Science 100, 1–3.Google Scholar
Saatchi, M, McClure, MC, McKay, SD, Rolf, MM, Kim, J, Decker, JE, Taxis, TM, Chapple, RH, Ramey, HR, Northcutt, SL, Bauck, S, Woodward, B, Dekkers, JCM, Fernando, RL, Schnabel, RD, Garrick, DJ and Taylor, JF 2011. Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation. Genetics Selection Evolution 43, 40.Google Scholar
Saatchi, M, Schnabel, RD, Rolf, MM, Taylor, JF and Garrick, DJ 2012. Accuracy of direct genomic breeding values for nationality evaluated traits in US Limousin and Simmental beef cattle. Genetics Selection Evolution 44, 38.Google Scholar
Silva, RMO, Fragomeni, BO, Lourenco, DAL, Magalhaes, AFB, Irano, N, Carvalheiro, R, Canesin, RC, Mercadante, MEZ, Boligon, AA, Baldi, FS, Misztal, I and Albuquerque, LG 2016. Accuracies of genomic prediction of feed efficiency traits using different prediction methods in an experimental Nelore cattle population. Journal of Animal Science 94, 3613–3623.CrossRefGoogle Scholar
Toosi, A, Fernando, RL and Dekkers, JCM 2010. Genomic selection in admixed and crossbred populations. Journal of Animal Science 88, 32–46.Google Scholar
Tussell, L, Gilbert, H, Riquet, J, Mercat, MJ, Legarra, A and Larzul, C 2016. Pedigree and genomic evaluation of pigs using a terminal-cross model. Genetics Selection Evolution 48, 32.CrossRefGoogle Scholar
Vallejo, RL, Leeds, TD, Gao, G, Parsons, JE, Martin, KE, Evenhuis, JP, Fragomen, BO, Wiens, GD and Palti, Y 2017. Genomic selection models double the accuracy of predicted breeding values for bacterial cold water disease resistance compared to a traditional pedigree-based model in rainbow trout aquaculture. Genetics Selection Evolution 49, 17.CrossRefGoogle ScholarPubMed
Van den Berg, I, Boichard, D and Lund, MS 2016. Sequence variants selected from a multi-breed GWAS can improve the reliability of genomic predictions in dairy cattle. Genetics Selection Evolution 48, 83.Google Scholar
Van Eenennaam, AL, Weigel, KA, Young, AE, Cleveland, MA and Dekkers, JCM 2014. Applied animal genomics: results from the field. Annual Review of Animal Biosciences 2, 105–139.CrossRefGoogle ScholarPubMed
Weber, KL, Thallman, RM, Keele, JW, Snelling, W M, Bennett, GL, Smith, TPL, McDaneld, TG, Allan, MF, Van Eenennaam, AL and Kuehn, LA 2012. Accuracy of genomic breeding values in multibreed beef cattle populations derived from deregressed breeding values and phenotypes. Journal of Animal Science 90, 4177–4190.Google Scholar
Wientjes, YC, Calus, MP, Goddard, ME and Hayes, BJ 2015. Impact of QTL properties on the accuracy of multi-breed genomic prediction. Genetics Selection Evolution 47, 42.Google Scholar
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