Genetic evaluation for three-way crossbreeding

BackgroundCommercial pig producers generally use a terminal crossbreeding system with three breeds. Many pig breeding organisations have started to use genomic selection for which genetic evaluation is often done by applying single-step methods for which the pedigree-based additive genetic relationship matrix is replaced by a combined relationship matrix based on both marker genotypes and pedigree. Genomic selection is implemented for purebreds, but it also offers opportunities for incorporating information from crossbreds and selecting for crossbred performance. However, models for genetic evaluation for the three-way crossbreeding system have not been developed.ResultsFour-variate models for three-way terminal crossbreeding are presented in which the first three variables contain the records for the three pure breeds and the fourth variable contains the records for the three-way crossbreds. For purebred animals, the models provide breeding values for both purebred and crossbred performances. Heterogeneity of genetic architecture between breeds and genotype by environment interactions are modelled through genetic correlations between these breeding values. Specification of the additive genetic relationships is essential for these models and can be defined either within populations or across populations. Based on these two types of additive genetic relationships, both pedigree-based, marker-based and combined relationships based on both pedigree and marker information are presented. All these models for three-way crossbreeding can be formulated using Kronecker matrix products and therefore fitted using Henderson’s mixed model equations and standard animal breeding software.ConclusionsModels for genetic evaluation in the three-way crossbreeding system are presented. They provide estimated breeding values for both purebred and crossbred performances, and can use pedigree-based or marker-based relationships, or combined relationships based on both pedigree and marker information. This provides a framework that allows information from three-way crossbred animals to be incorporated into a genetic evaluation system.

[1]  C. Cockerham,et al.  Gene effects and variances in hybrid populations. , 1966, Genetics.

[2]  C. R. Henderson A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values , 1976 .

[3]  J. Werf,et al.  Relationship between purebred and crossbred parameters. 2. Genetic correlation between purebred and crossbred performance under the model with two loci. , 1991 .

[4]  J. Werf,et al.  Maximizing genetic response in crossbreds using both purebred and crossbred information , 1994 .

[5]  J. V. D. van der Werf,et al.  Genetic correlation and heritabilities for purebred and crossbred performance in poultry egg production traits. , 1995, Journal of animal science.

[6]  J. Sölkner,et al.  Correlation between purebred and crossbred performance under a two-locus model with additive by additive interaction. , 1997, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[7]  H. Täubert,et al.  Parameter estimates for purebred and crossbred performances in pigs , 1998 .

[8]  M. Goddard,et al.  Prediction of total genetic value using genome-wide dense marker maps. , 2001, Genetics.

[9]  H. H. Timm,et al.  Genetic parameter estimates from joint evaluation of purebreds and crossbreds in swine using the crossbred model. , 2001, Journal of animal science.

[10]  Jean-Jacques Colleau,et al.  An indirect approach to the extensive calculation of relationship coefficients , 2002, Genetics Selection Evolution.

[11]  R. Fernando,et al.  Covariance between relatives in multibreed populations: additive model , 1993, Theoretical and Applied Genetics.

[12]  L. A. García-Cortés,et al.  Multibreed analysis by splitting the breeding values , 2006, Genetics Selection Evolution.

[13]  I Misztal,et al.  Genetic correlations between two strains of Durocs and crossbreds from differing production environments for slaughter traits. , 2007, Journal of animal science.

[14]  M. Lund,et al.  Genomic prediction when some animals are not genotyped , 2010, Genetics Selection Evolution.

[15]  R. Fernando,et al.  Genomic selection of purebreds for crossbred performance , 2009, Genetics Selection Evolution.

[16]  I Misztal,et al.  A relationship matrix including full pedigree and genomic information. , 2009, Journal of dairy science.

[17]  I Misztal,et al.  Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. , 2010, Journal of dairy science.

[18]  J. Hickey,et al.  Reciprocal Recurrent Genomic Selection for Total Genetic Merit in Crossbred Individuals , 2010 .

[19]  G. Kövér,et al.  Genetic Parameters of Growth Traits from a Joint Evaluation of Purebred and Crossbred Pigs , 2011 .

[20]  E. Knol,et al.  Heat stress effects on farrowing rate in sows: genetic parameter estimation using within-line and crossbred models. , 2012, Journal of animal science.

[21]  O. F. Christensen,et al.  Compatibility of pedigree-based and marker-based relationship matrices for single-step genetic evaluation , 2012, Genetics Selection Evolution.

[22]  P Madsen,et al.  Single-step methods for genomic evaluation in pigs. , 2012, Animal : an international journal of animal bioscience.

[23]  R. Fernando,et al.  Genomic selection of purebred animals for crossbred performance in the presence of dominant gene action , 2013, Genetics Selection Evolution.

[24]  Guosheng Su,et al.  Genomic evaluation of both purebred and crossbred performances , 2014, Genetics Selection Evolution.

[25]  F. Schenkel,et al.  A new approach for efficient genotype imputation using information from relatives , 2014, BMC Genomics.

[26]  Ignacy Misztal,et al.  Ancestral Relationships Using Metafounders: Finite Ancestral Populations and Across Population Relationships , 2015, Genetics.

[27]  D. Gianola,et al.  Genomic Heritability: What Is It? , 2014, PLoS genetics.