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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: llibertat.tusell@gmail.com
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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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