Abstract
BackgroundThe single-step single nucleotide polymorphism best linear unbiased prediction (ssSNPBLUP) method, such as single-step genomic BLUP (ssGBLUP), simultaneously analyses phenotypic, pedigree, and genomic information of genotyped and non-genotyped animals. In contrast to ssGBLUP, SNP effects are fitted explicitly as random effects in the ssSNPBLUP model. Similarly, principal components associated with the genomic information can be fitted explicitly as random effects in a single-step principal component BLUP (ssPCBLUP) model to remove noise in genomic information. Single-step genomic BLUP is solved efficiently by using the preconditioned conjugate gradient (PCG) method. Unfortunately, convergence issues have been reported when solving ssSNPBLUP by using PCG. Poor convergence may be linked with poor spectral condition numbers of the preconditioned coefficient matrices of ssSNPBLUP. These condition numbers, and thus convergence, could be improved through the deflated PCG (DPCG) method, which is a two-level PCG method for ill-conditioned linear systems. Therefore, the first aim of this study was to compare the properties of the preconditioned coefficient matrices of ssGBLUP and ssSNPBLUP, and to document convergence patterns that are obtained with the PCG method. The second aim was to implement and test the efficiency of a DPCG method for solving ssSNPBLUP and ssPCBLUP.ResultsFor two dairy cattle datasets, the smallest eigenvalues obtained for ssSNPBLUP (ssPCBLUP) and ssGBLUP, both solved with the PCG method, were similar. However, the largest eigenvalues obtained for ssSNPBLUP and ssPCBLUP were larger than those for ssGBLUP, which resulted in larger condition numbers and in slow convergence for both systems solved by the PCG method. Different implementations of the DPCG method led to smaller condition numbers, and faster convergence for ssSNPBLUP and for ssPCBLUP, by deflating the largest unfavourable eigenvalues.ConclusionsPoor convergence of ssSNPBLUP and ssPCBLUP when solved by the PCG method are related to larger eigenvalues and larger condition numbers in comparison to ssGBLUP. These convergence issues were solved with a DPCG method that annihilates the effect of the largest unfavourable eigenvalues of the preconditioned coefficient matrix of ssSNPBLUP and of ssPCBLUP on the convergence of the PCG method. It resulted in a convergence pattern, at least, similar to that of ssGBLUP.
Highlights
The single-step single nucleotide polymorphism best linear unbiased prediction method, such as single-step genomic BLUP, simultaneously analyses phenotypic, pedigree, and genomic information of genotyped and non-genotyped animals
Comparison of estimates of different single‐step BLUP Estimates for all fixed effects, additive genetic effects, and other possible random effects, of single-step genomic BLUP (ssGBLUP) solved with the preconditioned conjugate gradient (PCG) method, of ssSNPBLUP solved with the PCG and deflated PCG (DPCG) methods, and of ssPCBLUP solved with the PCG and DPCG methods, were the same after convergence was reached
We showed that convergence issues observed with ssSNPBLUP and ssPCBLUP solved by the PCG method are related with larger eigenvalues and larger effective spectral condition numbers in comparison to ssGBLUP
Summary
The single-step single nucleotide polymorphism best linear unbiased prediction (ssSNPBLUP) method, such as single-step genomic BLUP (ssGBLUP), simultaneously analyses phenotypic, pedigree, and genomic information of genotyped and non-genotyped animals. Some methods were proposed to approximate the inverse of G , such as the algorithm for proven and young animals (APY) [6], or to compute its inverse implicitly based on singular value decomposition (SVD) [7] or on the Woodbury decomposition [8] Another approach to avoid the computation of the inverse of G , or even G itself, is to fit the SNP effects explicitly, or principal components obtained from a SVD of the genotype matrix, as random effects in the model. A linear system of equations of single-step principal component BLUP (ssPCBLUP) has never been solved with the PCG method for large datasets
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