Technical note: Equivalent genomic models with a residual polygenic effect.

Routine genomic evaluations in animal breeding are usually based on either a BLUP with genomic relationship matrix (GBLUP) or single nucleotide polymorphism (SNP) BLUP model. For a multi-step genomic evaluation, these 2 alternative genomic models were proven to give equivalent predictions for genomic reference animals. The model equivalence was verified also for young genotyped animals without phenotypes. Due to incomplete linkage disequilibrium of SNP markers to genes or causal mutations responsible for genetic inheritance of quantitative traits, SNP markers cannot explain all the genetic variance. A residual polygenic effect is normally fitted in the genomic model to account for the incomplete linkage disequilibrium. In this study, we start by showing the proof that the multi-step GBLUP and SNP BLUP models are equivalent for the reference animals, when they have a residual polygenic effect included. Second, the equivalence of both multi-step genomic models with a residual polygenic effect was also verified for young genotyped animals without phenotypes. Additionally, we derived formulas to convert genomic estimated breeding values of the GBLUP model to its components, direct genomic values and residual polygenic effect. Third, we made a proof that the equivalence of these 2 genomic models with a residual polygenic effect holds also for single-step genomic evaluation. Both the single-step GBLUP and SNP BLUP models lead to equal prediction for genotyped animals with phenotypes (e.g., reference animals), as well as for (young) genotyped animals without phenotypes. Finally, these 2 single-step genomic models with a residual polygenic effect were proven to be equivalent for estimation of SNP effects, too.

[1]  A. Legarra,et al.  Single-Step Genomic Evaluations with 570K Genotyped Animals in US Holsteins , 2015 .

[2]  P. VanRaden,et al.  Technical note: Rapid calculation of genomic evaluations for new animals. , 2015, Journal of dairy science.

[3]  M. Lund,et al.  Comparison of genomic predictions using genomic relationship matrices built with different weighting factors to account for locus-specific variances. , 2014, Journal of dairy science.

[4]  R. Fernando,et al.  A class of Bayesian methods to combine large numbers of genotyped and non-genotyped animals for whole-genome analyses , 2014, Genetics Selection Evolution.

[5]  Z Liu,et al.  A single-step genomic model with direct estimation of marker effects. , 2014, Journal of dairy science.

[6]  Yi-Ping Phoebe Chen,et al.  A computationally efficient algorithm for genomic prediction using a Bayesian model , 2014, Genetics Selection Evolution.

[7]  F. Reinhardt,et al.  A Continuous Genomic Evaluation System for German Holsteins , 2014 .

[8]  I Misztal,et al.  Using recursion to compute the inverse of the genomic relationship matrix. , 2014, Journal of dairy science.

[9]  Jeremy F. Taylor,et al.  Implementation and accuracy of genomic selection , 2014 .

[10]  M. Calus,et al.  Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding , 2013, Genetics.

[11]  A Legarra,et al.  Computational strategies for national integration of phenotypic, genomic, and pedigree data in a single-step best linear unbiased prediction. , 2012, Journal of dairy science.

[12]  M. Lund,et al.  Genomic prediction for Nordic Red Cattle using one-step and selection index blending. , 2012, Journal of dairy science.

[13]  Ismo Strandén,et al.  Allele coding in genomic evaluation , 2011, Genetics Selection Evolution.

[14]  F. Seefried,et al.  Impacts of both reference population size and inclusion of a residual polygenic effect on the accuracy of genomic prediction , 2011, Genetics Selection Evolution.

[15]  Ignacy Misztal,et al.  Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information , 2011, Genetics Selection Evolution.

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

[17]  I Misztal,et al.  Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. , 2009, Journal of dairy science.

[18]  D. Garrick,et al.  Technical note: Derivation of equivalent computing algorithms for genomic predictions and reliabilities of animal merit. , 2009, Journal of dairy science.

[19]  M. Goddard,et al.  Invited review: Genomic selection in dairy cattle: progress and challenges. , 2009, Journal of dairy science.

[20]  P. VanRaden,et al.  Efficient methods to compute genomic predictions. , 2008, Journal of dairy science.

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

[22]  D. Harville Matrix Algebra From a Statistician's Perspective , 1998 .

[23]  E. B. Burnside,et al.  Genetic evaluation for herd life in Canada. , 1998, Journal of dairy science.