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Genome-enabled methods for predicting litter size in pigs: a comparison

Published online by Cambridge University Press:  24 July 2013

L. Tusell ,
P. Pérez-Rodríguez ,
S. Forni ,
X.-L. Wu  and
D. Gianola
L. Tusell*
Affiliation:
Department of Animal Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
P. Pérez-Rodríguez
Affiliation:
Department of Animal Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA Colegio de Postgraduados, Km. 36.5 Carretera México, Texcoco, Montecillo, Estado de México, 56230, México
S. Forni
Affiliation:
Genus Plc, Hendersonville, TN, USA
X.-L. Wu
Affiliation:
Department of Animal Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA Department of Dairy Science, University of Wisconsin, Madison, WI 53706, USA
D. Gianola
Affiliation:
Department of Animal Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA Department of Dairy Science, University of Wisconsin, Madison, WI 53706, USA Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI 53706, USA
*
†E-mail: [email protected]
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Abstract

Predictive ability of models for litter size in swine on the basis of different sources of genetic information was investigated. Data represented average litter size on 2598, 1604 and 1897 60K genotyped sows from two purebred and one crossbred line, respectively. The average correlation (r) between observed and predicted phenotypes in a 10-fold cross-validation was used to assess predictive ability. Models were: pedigree-based mixed-effects model (PED), Bayesian ridge regression (BRR), Bayesian LASSO (BL), genomic BLUP (GBLUP), reproducing kernel Hilbert spaces regression (RKHS), Bayesian regularized neural networks (BRNN) and radial basis function neural networks (RBFNN). BRR and BL used the marker matrix or its principal component scores matrix (UD) as covariates; RKHS employed a Gaussian kernel with additive codes for markers whereas neural networks employed the additive genomic relationship matrix (G) or UD as inputs. The non-parametric models (RKHS, BRNN, RNFNN) gave similar predictions to the parametric counterparts (average r ranged from 0.15 to 0.23); most of the genome-based models outperformed PED (r = 0.16). Predictive abilities of linear models and RKHS were similar over lines, but BRNN varied markedly, giving the best prediction (r = 0.31) when G was used in crossbreds, but the worst (r = 0.02) when the G matrix was used in one of the purebred lines. The r values for RBFNN ranged from 0.16 to 0.23. Predictive ability was better in crossbreds (0.26) than in purebreds (0.15 to 0.22). This may be related to family structure in the purebred lines.

Keywords

genomic predictionBayesian regressionRKHSneural networkslitter size
Type
Breeding and genetics
Information
animal , Volume 7 , Issue 11 , November 2013 , pp. 1739 - 1749
Copyright
Copyright © The Animal Consortium 2013 

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References

Bates, D, Maechler, M, Bolker, B 2012. lme4: linear mixed-effects models using S4 classes. R package version 2.15.1. Retrieved February 12, 2013, from http://CRAN.R-project.org/package=lme4.Google Scholar
Broomhead, DS, Lowe, D 1988. Multi-variable functional interpolation and adaptive networks. Complex Systems 2, 321–355.Google Scholar
Chen, S, Cowan, CF, Grant, PM 1991. Orthogonal least squares learning algorithms for radial basis function networks. IEEE Trans. Neural Networks 2, 302–309.Google Scholar
Cleveland, MA, Forni, S, Garrick, DJ, Deeb, N 2010. Prediction of genomic breeding values in a commercial pig population. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production August 1, 2010, Leipzig, Germany.Google Scholar
Crossa, J, de los Campos, G, Pérez, P, Gianola, D, Burgueño, J, Araus, JL, Makumbi, D, Singh, RP, Dreisigacker, S, Yan, J, Arief, V, Banziger, M, Braun, H-J 2010. Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186, 713–724.Google Scholar
Daetwyler, HD, Kemper, KE, van der Werf, JHJ, Hayes, BJ 2012. Components of the accuracy of genomic prediction in a multi-breed sheep population. Journal of Animal Science 90, 3375–3384.Google Scholar
de los Campos, G, Pérez, P 2012. BLR: bayesian linear regression. R package version 1.3. Retrieved February 12, 2013, from http://CRAN.R-project.org/package=BLR.Google Scholar
de los Campos, G, Gianola, D, Rosa, GJM 2009a. Reproducing kernel Hilbert spaces regression: a general framework for genetic evaluation. Journal of Animal Science 87, 1883–1887.CrossRefGoogle ScholarPubMed
de los, Campos, Naya, H, Gianola, D, Crossa, J, Legarra, A, Manfredi, E, Weigel, K, Cotes, JM 2009b. Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182, 375–385.Google Scholar
de los Campos, G, Gianola, D, Guilherme, JMR, Kent, W, Crossa, J 2010. Semi parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. Genetics Research 92, 295–308.Google Scholar
de los Campos, G, Hickey, JM, Pong-Wong, R, Daetwyler, HD, Calus, MPL 2012a. Whole genome regression and prediction methods applied to plant and animal breeding. Genetics 193, 327–345.Google Scholar
de los Campos, G, Vazquez, A, Klimentidis, YC, Sorensen, D 2012b. Whole-genome regression and prediction of human complex traits using data from related and unrelated individuals. Proceedings of the 4th International Conference on Quantitative Genetics: Understanding Variation in Complex Traits, June 17, 2012, Edinburgh, UK.Google Scholar
Dekkers, JCM 2004. Commercial application of marker- and gene-assisted selection in livestock: strategies and lessons. Journal of Animal Science 82, E313–E328.Google ScholarPubMed
Falconer, DS, MacKay, TFC 1996. Introduction to quantitative genetics, 4th edition. Longman Scientific & Technical, Burnt Mill, Harlow, UK.Google Scholar
Forni, S, Aguilar, I, Misztal, I, Deeb, N 2010. Genomic relationships and biases in the evaluation of sow litter size. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, August 1, 2010, Leipzig, Germany, 2–152pp.Google Scholar
Gianola, D, Fernando, RL, Stella, A 2006. Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics 173, 1761–1776.Google Scholar
Gianola, D, Hayrettin, O, Weigel, KA, Rosa, GJM 2011. Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat. BMC Genetics 12, 87.CrossRefGoogle ScholarPubMed
González-Camacho, J, de los Campos, G, Pérez, P, Gianola, D, Cairns, J, Mahuku, G, Babu, R, Crossa, J 2012. Genome-enabled prediction of genetic values using radial basis function neural networks. Theoretical and Applied Genetics 125, 759–771.CrossRefGoogle ScholarPubMed
González-Recio, O, Gianola, D, Long, N, Weigel, KA, Rosa, GJM, Avendaño, S 2008. Nonparametric methods for incorporating genomic information into genetic evaluations: an application to mortality in broilers. Genetics 178, 2305–2313.Google Scholar
Habier, D, Fernando, R, Kizilkaya, K, Garrick, D 2011. Extension of the bayesian alphabet for genomic selection. BMC Bioinformatics 12, 186.CrossRefGoogle ScholarPubMed
Habier, D, Tetens, J, Seefried, F-R, Lichtner, P, Thaller, G 2010. The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genetics Selection Evolution 42, 5.CrossRefGoogle ScholarPubMed
Hayes, B, Bowman, P, Chamberlain, A, Verbyla, K, Goddard, M 2009. Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genetics Selection Evolution 41, 51.Google Scholar
Heslot, N, Yang, HP, Sorrells, ME, Jannink, JL 2012. Genomic selection in plant breeding: a comparison of models. Crop Science 52, 146–160.Google Scholar
Ibañez-Escriche, N, Gonzalez-Recio, O 2011. Review. Promises, pitfalls and challenges of genomic selection in breeding programs. Spanish Journal of Agricultural Research 9, 404–413.Google Scholar
Janss, L, de los Campos, G, Sheehan, N, Sorensen, D 2012. Inferences from genomic models in stratified populations. Genetics 192, 693–704.Google Scholar
Lillehammer, M, Meuwissen, THE, Sonesson, AK 2011. Genomic selection for maternal traits in pigs. Journal of Animal Science 89, 3908–3916.Google Scholar
MacKay, DJC 1992. Bayesian interpolation. Neural Computation 4, 415–447.Google Scholar
Maltecca, C, Parker, KL, Cassady, JP 2012. Application of multiple shrinkage methods to genomic predictions. Journal of Animal Science 90, 1777–1787.Google Scholar
Mandel, J 1982. Use of the singular value decomposition in regression analysis. The American Statistician 36, 15–24.Google Scholar
Okut, H, Gianola, D, Rosa, GJM, Weigel, KA 2011. Prediction of body mass index in mice using dense molecular markers and a regularized neural network. Genetics Research 93, 189–201.Google Scholar
Park, T, Casella, G 2008. The Bayesian lasso. Journal of the American Statistical Association 103, 681–686.CrossRefGoogle Scholar
Piepho, HP 2009. Ridge regression and extensions for genomewide selection in maize. Crop Science 49, 1165–1176.Google Scholar
Pérez, P, de los Campos, G, Crossa, J, Gianola, D 2010. Genomic-enabled prediction based on molecular markers and pedigree using the Bayesian linear regression package in R. Plant Genome 3, 106–116.Google Scholar
Pérez-Rodríguez, P, Gianola, D, Weigel, KA, Rosa, GJM, Crossa, J 2013. Technical note: An R package for fitting Bayesian regularized neural networks with applications in animal breeding. Published online doi: 10.2527/jas.2012-6162. J ANIM SCI May 8, 2013 jas.2012-6162.Google Scholar
Pérez-Rodríguez, P, Gianola, D, González-Camacho, JM, Crossa, J, Manès, Y, Dreisigacker, S 2012. Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat. G3: Genes Genomes Genetics 2, 1595–1605.Google Scholar
R Core Team 2012. R: A Language and Environment for Statistical Computing. ISBN 3-900051-07-0. R Core Team, Vienna, Austria. Retrieved February 12, 2013, from http://www.R-project.org/.Google Scholar
VanRaden, PM 2008. Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.Google Scholar
VanRaden, PM, Van Tassell, CP, Wiggans, GR, Sonstegard, TS, Schnabel, RD, Taylor, JF, Schenkel, FS 2009. Invited review: reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science 92, 16–24.Google Scholar