A single-step genomic model with direct estimation of marker effects

• Published in: Journal of dairy science 2014-09, Vol.97 (9), p.5833-5850
• Main Authors: Liu, Z., Goddard, M.E., Reinhardt, F., Reents, R.
• Format: Article
• Language: English
• Subjects: Algorithms; Animals; Breeding - methods; Computer Simulation; dairy cattle; Dairying - methods; Genetic Markers; genomic prediction; Genomics - methods; Genotype; mixed model equation; Models, Genetic; Pedigree; Polymorphism, Single Nucleotide - genetics; Regression Analysis; single-step SNP model; Software
• ISSN: 0022-0302; 1525-3198
• DOI: 10.3168/jds.2014-7924

Compared with the currently widely used multi-step genomic models for genomic evaluation, single-step genomic models can provide more accurate genomic evaluation by jointly analyzing phenotypes and genotypes of all animals and can properly correct for the effect of genomic preselection on genetic evaluations. The objectives of this study were to introduce a single-step genomic model, allowing a direct estimation of single nucleotide polymorphism (SNP) effects, and to develop efficient computing algorithms for solving equations of the single-step SNP model. We proposed an alternative to the current single-step genomic model based on the genomic relationship matrix by including an additional step for estimating the effects of SNP markers. Our single-step SNP model allowed flexible modeling of SNP effects in terms of the number and variance of SNP markers. Moreover, our single-step SNP model included a residual polygenic effect with trait-specific variance for reducing inflation in genomic prediction. A kernel calculation of the SNP model involved repeated multiplications of the inverse of the pedigree relationship matrix of genotyped animals with a vector, for which numerical methods such as preconditioned conjugate gradients can be used. For estimating SNP effects, a special updating algorithm was proposed to separate residual polygenic effects from the SNP effects. We extended our single-step SNP model to general multiple-trait cases. By taking advantage of a block-diagonal (co)variance matrix of SNP effects, we showed how to estimate multivariate SNP effects in an efficient way. A general prediction formula was derived for candidates without phenotypes, which can be used for frequent, interim genomic evaluations without running the whole genomic evaluation process. We discussed various issues related to implementation of the single-step SNP model in Holstein populations with an across-country genomic reference population.