Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-16T07:27:35.818Z Has data issue: false hasContentIssue false
  • English
  • Français

Genomic selection in a pig population including information from slaughtered full sibs of boars within a sib-testing program

Published online by Cambridge University Press:  16 December 2014

A. B. Samorè ,
L. Buttazzoni ,
M. Gallo ,
V. Russo  and
L. Fontanesi
A. B. Samorè*
Affiliation:
Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, 40127 Bologna, Italy
L. Buttazzoni
Affiliation:
Centro di Ricerca per la Produzione delle Carni e il Miglioramento Genetico, Consiglio per la Ricerca e la Sperimentazione in Agricoltura, Monterotondo Scalo, 00016 Roma, Italy
M. Gallo
Affiliation:
Associazione Nazionale Allevatori Suini, 00161 Roma, Italy
V. Russo
Affiliation:
Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, 40127 Bologna, Italy
L. Fontanesi
Affiliation:
Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, 40127 Bologna, Italy
*
†E-mail: [email protected]
Get access

Abstract

Genomic selection is becoming a common practise in dairy cattle, but only few works have studied its introduction in pig selection programs. Results described for this species are highly dependent on the considered traits and the specific population structure. This paper aims to simulate the impact of genomic selection in a pig population with a training cohort of performance-tested and slaughtered full sibs. This population is selected for performance, carcass and meat quality traits by full-sib testing of boars. Data were simulated using a forward-in-time simulation process that modeled around 60K single nucleotide polymorphisms and several quantitative trait loci distributed across the 18 porcine autosomes. Data were edited to obtain, for each cycle, 200 sires mated with 800 dams to produce 800 litters of 4 piglets each, two males and two females (needed for the sib test), for a total of 3200 newborns. At each cycle, a subset of 200 litters were sib tested, and 60 boars and 160 sows were selected to replace the same number of culled male and female parents. Simulated selection of boars based on performance test data of their full sibs (one castrated brother and two sisters per boar in 200 litters) lasted for 15 cycles. Genotyping and phenotyping of the three tested sibs (training population) and genotyping of the candidate boars (prediction population) were assumed. Breeding values were calculated for traits with two heritability levels (h2=0.40, carcass traits, and h2=0.10, meat quality parameters) on simulated pedigrees, phenotypes and genotypes. Genomic breeding values, estimated by various models (GBLUP from raw phenotype or using breeding values and single-step models), were compared with the classical BLUP Animal Model predictions in terms of predictive ability. Results obtained for traits with moderate heritability (h2=0.40), similar to the heritability of traits commonly measured within a sib-testing program, did not show any benefit from the introduction of genomic selection. None of the considered genomic models provided improvements in prediction ability of pigs with no recorded phenotype. However, a few advantages were found for traits with low heritability (h2=0.10). These heritability levels are characteristic for meat quality traits recorded after slaughtering or for reproduction or health traits, typically recorded on field and not in performance stations. Other scenarios of data recording and genotyping should be evaluated before considering the implementation of genomic selection in a pig-selection scheme based on sib testing of boars.

Keywords

selection programheritabilitypredictive abilityGEBV
Type
Research Article
Information
animal , Volume 9 , Issue 5 , May 2015 , pp. 750 - 759
Copyright
© The Animal Consortium 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aguilar, I, Misztal, I, Johnson, DL, Legarra, A, Tsuruta, S and Lawlor, TJ 2010. Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science 93, 743–752.Google Scholar
Akanno, EC, Schenkel, FS, Sargolzaei, M, Friendship, RM and Robinson, JAB 2014. Persistency of accuracy of genomic breeding values for different simulated pig breeding programs in developing countries. Journal of Animal Breeding and Genetics 131, 367–378.Google Scholar
Christensen, OF, Madsen, P, Nielsen, B, Ostersen, T and Su, G 2012. Single-step methods for genomic evaluation in pigs. Animal 6, 1565–1571.Google Scholar
Clark, SA, Hickey, JM and van der Werf, JHJ 2011. Different models of genetic variation and their effect on genomic evaluation. Genetics Selection Evolution 43, 18.Google Scholar
Daetwyler, HD, Pong-Wong, R, Villanueva, B and Woolliams, JA 2010. The impact of genomic architecture on genome-wide evaluation methods. Genetics 185, 1021–2031.Google Scholar
Dekkers, JCM, Mathur, PK and Knol, E 2011. Genetic improvement of the pig. In The genetics of the pig, 2nd edition (ed. MF Rothschild and A Ruvinsky), pp. 390–425. CAB International, Wallingford, UK.Google Scholar
De los Campos, G, Naya, H, Gianola, D, Crossa, J, Legarra, A, Manfredi, E, Weigel, K and Cotes, JM 2009. Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182, 375–385.Google Scholar
Fontanesi, L, Buttazzoni, L, Galimberti, G, Calò, DG, Scotti, E and Russo, V 2013. Association between melanocortin 4 receptor (MC4R) gene haplotypes and carcass and production traits in Italian large white pigs evaluated with a selective genotyping approach. Livestock Science 157, 48–56.Google Scholar
Fontanesi, L, Speroni, C, Buttazzoni, L, Scotti, E, Dall’Olio, S, Nanni Costa, L, Davoli, R and Russo, V 2010. The insulin-like growth factor 2 (IGF2) gene intron3-g.3072G>A polymorphism is not the only Sus scrofa chromosome 2p mutation affecting meat production and carcass traits in pigs: evidence from the effects of a cathepsin D (CTSD) gene polymorphism. Journal of Animal Science 88, 2235–2245.Google Scholar
Fontanesi, L, Schiavo, G, Galimberti, G, Calò, DG, Scotti, E, Martelli, PL, Buttazzoni, L, Casadio, R and Russo, V 2012. A genome wide association study for backfat thickness in Italian large white pigs highlights new regions affecting fat deposition including neuronal genes. BMC Genomics 13, 583.Google Scholar
Garrick, DJ, Taylor, JF and Fernando, RL 2009. Deregressing estimated breeding values and weighting information for genomic regression analyses. Genetics Selection Evolution 41, 55.CrossRefGoogle ScholarPubMed
Groenen, MAM, Archibald, AL, Uenishi, H, Tuggle, CK, Takeuki, Y, Rthshild, MF, Rogel-Gailard, C, Park, C, Milan, D, Megens, H, Li, S, Larkin, DM, Kim, H, Frantz, LAF, Caccamo, M, Ahn, H, Aken, BL, Anselmo, A, Anthon, C, Auvil, L, Badaoui, B, Beattie, CW, Bendixen, C, Berman, D, Blecha, F, Blomberg, J, Bolund, L, Bosse, M, Botti, S, Bujie, Z, Bystrom, M, Capitanu, B, Carvalho-Silva, D, Chardon, P, Chen, C, Cheng, R, Choi, SH, Chow, W, Clark, RC, Clee, C, Crooijmans, RPMA, Dawson, HD, Dehais, P, De Sapio, F, Dibbits, B, Drou, N, Du, ZQ, Eversole, K, Fadista, J, Fairley, S, Faraut, T, Faulkner, GJ, Fowler, KE, Fredholm, M, Fritz, E, Gilbert, JGR, Giuffra, E, Gorodkin, J, Griffin, DK, Harrow, JL, Hayward, A, Howe, K, Hu, ZL, Humphray, SJ, Hunt, T, Hornshøj, H, Jeon, JT, Jern, P, Jones, M, Jurka, J, Kanamori, H, Kapetanovic, R, Kim, J, Kim, JH, Kim, KW, Kim, TH, Larson, G, Lee, K, Lee, KT, Leggett, R, Lewin, HA, Li, Y, Liu, W, Loveland, JE, Lu, Y, Lunney, JK, Ma, J, Madsen, O, Mann, K, Matthews, L, McLaren, S, Morozumi, T, Murtaugh, MP, Narayan, J, Nguyen, DT, Ni, P, Oh, SJ, Onteru, S, Panitz, F, Park, EW, Park, HS, Pascal, G, Paudel, Y, Perez-Enciso, M, Ramirez-Gonzalez, R, Reecy, JM, Rodriguez-Zas, S, Rohrer, GA, Rund, L, Sang, Y, Schachtschneider, K, Schraiber, JG, Schwartz, J, Scobie, L, Scott, C, Searle, S, Servin, B, Southey, BR, Sperber, G, Stadler, P, Sweedler, JV, Tafer, H, Thomsen, B, Wali, R, Wang, J, Wang, J, White, S, Xu, X, Yerle, M, Zhang, G, Zhang, J, Zhang, J, Zhao, S, Rogers, J, Churcher, C and Lawrence, B 2012. Analyses of pig genomes provide insight into porcine demography and evolution. Nature 491, 393–398.CrossRefGoogle ScholarPubMed
Habier, D, Fernando, RL, Kizilkaya, K and Garrick, DJ 2011. Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics 12, 186.CrossRefGoogle ScholarPubMed
Habier, D, Tetens, J, Seefried, FR, Lichtner, P and Thaller, G 2010. The impact of genetic relationship information on genomic breeding values in German holstein cattle. Genetics Selection Evolution 42, 5.Google Scholar
Hayes, BJ, Visscher, PM and Goddard, ME 2009a. Increased accuracy of artificial selection by using the realized relationship matrix. Genetics Research 91, 47–60.Google Scholar
Hayes, BJ, Bowman, PJ, Chamberlain, AJ and Goddard, ME 2009b. Invited review: genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science 92, 433–443.Google Scholar
Hayes, BJ, Bowman, PJ, Chamberlain, AC, Verbyla, K and Goddard, ME 2009c. Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genetics Selection Evolution 41, 51.Google Scholar
Henderson, CR 1975. Best linear unbiased estimation and prediction under a selection model. Biometrics 31, 423–447.Google Scholar
Henryon, M, Berg, P, Ostersen, T, Nielsen, B and Sørensen, AC 2012. Most of the benefits from genomic selection can be realized by genotyping a small proportion of available candidates. Journal of Animal Science 90, 4681–4689.Google Scholar
Koenig, S and Swalve, HH 2009. Application of selection index calculations to determine selection strategies in genomic breeding programs. Journal of Dairy Science 92, 5292–5303.CrossRefGoogle Scholar
Legarra, A, Aguilar, I and Misztal, I 2009. A relationship matrix including full pedigree and genomic information. Journal of Dairy Science 92, 4656–4663.Google Scholar
Legarra, A, Ricard, A and Filangi, O 2013. GS3: genomic selection, Gibbs sampling, Gauss Seidel (and Bayes C). Retrieved September 30, 2013, from http://snp.toulouse.inra.fr/~alegarra/manualgs3_last.pdf Google Scholar
Lillehammer, M, Meuwissen, THE and Sonesson, AK 2011. Genomic selection for maternal trait in pigs. Journal of Animal Science 89, 3908–3916.Google Scholar
Meuwissen, THE, Hayes, BJ and Goddard, ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1929.Google Scholar
Misztal, I 2008. Review article: reliable computing in estimation of variance components. Journal of Animal Breeding and Genetics 125, 363–370.Google Scholar
Misztal, I, Legarra, A and Aguilar, I 2009. Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. Journal of Dairy Science 92, 4648–4655.Google Scholar
Ostersen, T, Christensen, OF, Henryon, M, Nielsen, B, Guosheng, S and Madsen, P 2011. Deregressed EBV as the response variable yield more reliable genomic predictions than traditional EBV in pure-bred pigs. Genetics Selection Evolution 43, 38.Google Scholar
Powell, JE, Visscher, PM and Goddard, ME 2010. Reconciling the analysis of IBD and IBS in complex trait studies. Nature Reviews Genetics 11, 800–805.CrossRefGoogle ScholarPubMed
Pryce, JE and Daetwyler, HD 2012. Designing dairy cattle breeding schemes under genomic selection: a review of international research. Animal Production Science 52, 107–114.Google Scholar
Ramos, AM, Crooijmans, RP, Affara, NA, Amaral, AJ, Archibald, AL, Beever, JE, Bendixen, C, Churcher, C, Clark, R, Dehais, P, Hansen, MS, Hedegaard, J, Hu, ZL, Kerstens, HH, Law, AS, Megens, HJ, Milan, D, Nonneman, DJ, Rohrer, GA, Rothschild, MF, Smith, TP, Schnabel, RD, Van Tassell, CP, Taylor, JF, Wiedmann, RT, Schook, LB and Groenen, MA 2009. Design of a high density SNP genotyping assay in the pig using SNPs identified and characterized by next generation sequencing technology. PLoS One 4, e6524.Google Scholar
Sargolzaei, M and Schenkel, FS 2009. QMSim: a large-scale genome simulator for livestock. Bioinformatics 25, 680–681.Google Scholar
Schaeffer, LR 2006. Strategy for applying the genome-wide selection in dairy cattle. Journal of Animal Breeding and Genetics 123, 218–223.Google Scholar
Sonesson, AK and Meuwissen, THE 2009. Testing strategies for genomic selection in aquaculture breeding programs. Genetics Selection Evolution 41, 37.CrossRefGoogle ScholarPubMed
Suzuki, K, Irie, M, Kadowaki, H, Shibata, T, Kumagai, M and Nishida, A 2005. Genetic parameter estimates of meat quality traits in Duroc pigs selected for average daily gain, longissimus muscle area, backfat thickness, and intramuscular fat content. Journal of Animal Science 83, 2058–2065.Google Scholar
Tribout, T, Larzul, C and Phocas, F 2012. Efficiency of genomic selection in a purebred pig male line. Journal of Animal Science 90, 4164–4176.Google Scholar
Tribout, T, Larzul, C and Phocas, F 2013. Economic aspects of implementing genomic evaluations in a pig sire line breeding scheme. Genetics Selection Evolution 45, 40.Google Scholar
Tribout, T, Bidanel, JP, Pochas, F, Schwob, S, Guillaume, F and Larzul, C 2011. La sélection génomique: principe et perspectives pour l’amélioration des populations porcines. In Proceedings of the 43th Journées de la Recherche Porcine en France, 15–16 February, Paris, France, pp. 13–25.Google Scholar
Van Laere, AS, Nguyen, M, Braunschweig, M, Nezer, C, Collette, C, Moreau, L, Archibald, AL, Haley, CS, Buys, N, Tally, M, Andersson, G, Georges, M and Andersson, L 2003. A regulatory mutation in IGF2 causes a major QTL effect on muscle growth in the pig. Nature 425, 832–836.Google Scholar
VanRaden, PM, Van Tassel, CP, Wiggans, GW, Sonstegard, TS, Schnabel, RD, Taylor, JF and Schenkel, F 2009. Invited review: reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science 92, 16–24.Google Scholar
Yi, N and Xu, S 2008. Bayesian LASSO for quantitative trait loci mapping. Genetics 179, 1045–1055.Google Scholar