[en] In the current post-genomic era, the genetic basis of pig growth can be understood by assessing SNP marker effects and genomic breeding values (GEBV) based on estimates of these growth curve parameters as phenotypes. Although various statistical methods, such as random regression (RR-BLUP) and Bayesian LASSO (BL), have been applied to genomic selection (GS), none of these has yet been used in a growth curve approach. In this work, we compared the accuracies of RR-BLUP and BL using empirical weight-age data from an outbred F2 (Brazilian Piau X commercial) population. The phenotypes were determined by parameter estimates using a nonlinear logistic regression model and the halothane gene was considered as a marker for evaluating the assumptions of the GS methods in relation to the genetic variation explained by each locus. BL yielded more accurate values for all of the phenotypes evaluated and was used to estimate SNP effects and GEBV vectors. The latter allowed the construction of genomic growth curves, which showed substantial genetic discrimination among animals in the final growth phase. The SNP effect estimates allowed identification of the most relevant markers for each phenotype, the positions of which were
coincident with reported QTL regions for growth traits.
Disciplines :
Animal production & animal husbandry
Author, co-author :
Fonseca e Silva, Fabyano
Vilela de Resende, Marcos Deon
Rocha, Gilson Silvério
Souza Duarte, Darlene Ana ; Université de Liège - ULiège > Agronomie, Bio-ingénierie et Chimie (AgroBioChem) > Ingénierie des productions animales et nutrition
Sávio Lopes, Paulo
Bernardes Brustolini, Otávio José
Thus, Sander
Soriano Viana, José Marcelo
Facioni Guimarães, Simone Eliza
Language :
English
Title :
Genomic growth curves of an outbred pig population
Publication date :
July 2013
Journal title :
Genetics and Molecular Biology
ISSN :
1415-4757
eISSN :
1678-4685
Publisher :
Sociedade Brasileira de Genetica, São Paulo, Brazil
Akanno, E.C., (2012) Genome-Wide Selection Program For Improvement of Indigenous Pigs In Tropical Developing Countries, p. 217. , Guelph University Press, Guelph
Band, G.O., Guimarães, S.E.F., Lopes, P.S., Peixoto, J.O., Faria, D.A., Pires, A.V., Figueredo, F.C., Gomide, L.A.M., Relationship between the Porcine Stress Syndrome gene and carcass and performance traits in F2 pigs resulting from divergent crosses (2005) Genet Mol Biol, 28, pp. 92-96
Cepica, S., Reiner, G., Bartenschlager, H., Moser, G., Geldermann, H., Linkage and QTL mapping for Sus scrofa chromosome X (2003) J Anim Breed Genet, 120, pp. 144-151
de los Campos, G., Naya, H., Gianola, D., Crossa, J., Legarra, A., Manfredi, E., Weige, K., Cotes, J.M., Predicting quan-Pig growth and SNP markers titative traits with regression models for dense molecular markers (2009) Genetics, 182, pp. 375-385
Endelman, J.B., Ridge regression and other kernels for genomic selection with R package rrBLUP (2011) Plant Genome, 4, pp. 250-255
Forni, S., Piles, M., Blasco, A., Varona, L., Oliveira, H.N., Lôbo, R.B., Albuquerque, L.G., Analysis of beef cattle longitudinal data applying a nonlinear model (2007) J Anim Sci, 85, pp. 3189-3197
Fujii, J., Otsu, K., Zorzato, F., de Leon, S., Khanna, V.K., Weiler, J.E., O'Brien, P.J., Maclennan, D.H., Identification of a mutation in the porcine ryanodine receptor associated with malignant hyperthermia (1991) Science, 253, pp. 448-451
Habier, D., Fernando, R.L., Dekkers, J.C.M., The impact of genetic relationship information on genome-assisted breeding values (2007) Genetics, 177, pp. 2389-2397
Ibáñez-Escriche, N., Blasco, A., Modifying growth curve parameters by multitrait genomic selection (2011) J Anim Sci, 89, pp. 661-668
Koivula, M., Sevón-Aimonen, M.L., Strandén, I., Matilainen, K., Serenius, T., Stalder, K.J., Mäntysaari, E.A., Genetic (co)variances and breeding value estimation of Gompertz growth curve parameters in Finnish Yorkshire boars, gilts and barrows (2008) J Anim Breed Genet, 125, pp. 168-175
Koning, D.J., Rattink, A.P., Harlizius, B., Groenen, M.A.M., Brascamp, E.W., van Arendonk, J.A.M., Detection and characterization of quantitative trait loci for growth and reproduction traits in pigs (2001) Livest Prod Sci, 72, pp. 185-198
Kusec, G., Baulain, U., Kallweit, E., Glodek, P., Influence of MHS genotype and feeding regime on allometric and temporal growth of pigs assessed by magnetic resonance imaging (2007) Livest Sci, 110, pp. 89-100
Meuwissen, T.H.E., Hayes, B.J., Goddard, M.E., Prediction of total genetic value using genome wide dense marker maps (2001) Genetics, 157, pp. 1819-1829
Meyer, K., Estimating covariance functions for longitudinal data using a random regression model (1998) Genet Sel Evol, 30, pp. 221-240
Mignon-Grasteau, S., Beaumont, C., Le Bihan-Duval, E., Poivey, J.P., de Rochambeau, H., Richard, F.H., Genetic parameters of growth curve parameters in male and female chickens (1999) Br Poultry Sci, 40, pp. 44-51
Miller, K.D., Ellis, M., McKeith, F.K., Wilson, E.R., Influence of sire line and halothane genotype on growth performance, carcass characteristics, and meat quality in pigs (2000) Can J Anim Sci, 80, pp. 319-327
Moser, G., Tier, B., Crump, R.E., Khatkar, M.S., Raadsma, H., A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers (2009) Genet Sel Evol, 41, pp. 56-64
Ogutu, J.O., Schulz-Streeck, T., Piepho, H.P., Genomic selection using regularized linear regression models: Ridge regression, LASSO, elastic net and their extensions (2012) BMC Proc, 6, pp. S10
Pérez, P., de los Campos, G., Crossa, J., Gianola, D., Genomic-enabled prediction based on molecular markers and pedigree using the Bayesian linear regression package in R (2010) Plant Genome, 3, pp. 106-116
Pierzchala, M., Cieslak, D., Reiner, G., Bartenschlager, H., Moser, G., Geldermann, H., Linkage and QTL mapping for Sus scrofa chromosome 17 (2003) J Anim Breed Genet, 120, pp. 132-137
Pong-Wong, R., Hadjipavlou, G.A., A two-step approach combining the Gompertz growth with genomic selection for longitudinal data (2010) BMC Proc, 4, pp. S4
Ratkowsky, D.A., (1983) Nonlinear Regression Modeling, p. 276. , Marcel Dekker, New York
Sanchez, M.P., Riquet, J., Iannuccelli, N., Gogue, J., Billon, Y., Demeure, O., Caritez, J.C., Bonnet, B., Effects of quantitative trait loci on chromosomes 1, 2, 4, and 7 on growth, carcass, and meat quality traits in backcross Meishan x Large White pigs (2006) J Anim Sci, 84, pp. 526-537
Silva, F.F., Rosa, G.J.M., Guimarães, S.E.F., Lopes, P.S., de los Campos, G., Three-step Bayesian factor analysis applied to QTL detection in crosses between outbred pig populations (2011) Livest Sci, 42, pp. 210-215
Smith, B.J., An R package for MCMC output convergence assessment and posterior inference J Stat Softw, 21, pp. 1-37
Su, Y.H., Xiong, Y.Z., Zhang, Q., Jiang, S.W., Yu, L., Lei, M.G., Zheng, R., Deng, C.Y., Mapping quantitative trait loci for fat deposition in carcass in pigs (2002) Acta Genet Sin, 29, pp. 607-611
Varona, L., Moreno, C., Garcia-Cortés, L.A., Yague, G., Altarriba, J., Two-step vs. Joint analysis of Von Bertalanffy function (1999) J Anim Breed Genet, 116, pp. 331-338
Yue, G., Stratil, A., Cepica, S., Schröffel Jr., J., Schröffelova, D., Fontanesi, L., Cagnazzo, M., Rei, G., Linkage and QTL mapping for Sus scrofa chromosome 7 (2003) J Anim Breed Genet, 120, pp. 56-65
