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

Abstract

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.

Statistical and Computational Challenges in Whole Genome Prediction and Genome-Wide Association Analyses for Plant and Animal Breeding

“…Furthermore, MCMC does not allow a form of 'memory' when re-running or updating analyses based on the collection of more data. As Allenby et al (2014) attests, "real-time" posterior densities are very difficult to get with big data using MCMC in an era where just-in-time analysis updates are increasingly demanded (Liu et al 2014). This is particularly true in animal breeding where inferences on breeding values are continually updated (Wiggans et al 2015).…”

Strategies for comparing and combining different genetic and genomic evaluations: A review

“…It is worth noting that another disadvantage of the single-step approaches is their high computational costs for computing the combined pedigree-genomic relationship matrices (and related matrices). Several alternative single-step approaches avoiding computational costs were proposed (e.g., Fernando et al, 2014;Gengler et al, 2012;Legarra and Ducrocq, 2012;Liu et al, 2014). A review of the single-step approaches and their alternatives was proposed by Legarra et al (2014).…”

Genomic selection in multi-breed dairy cattle populations

“…This material can be extended to an arbitrary number of breeds without loss of generality. While the discussion focuses on GBLUP, there are several other approaches that can be used to estimate genomic breeding values, including the singlestep approach (Misztal et al, 2009;Liu et al, 2014) and various Bayesian methods (Gianola et al, 2006;Moser et al, 2009;Habier et al, 2011).…”