Single-step SNP-BLUP with on-the-fly imputed genotypes and residual polygenic effects

Matti Taskinen,Esa A Mäntysaari,Ismo Strandén

doi:10.1186/s12711-017-0310-9

Matti Taskinen, Esa A Mäntysaari + Show 1 more

Open Access

https://doi.org/10.1186/s12711-017-0310-9

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Abstract

BackgroundSingle-step genomic best linear unbiased prediction (BLUP) evaluation combines relationship information from pedigree and genomic marker data. The inclusion of the genomic information into mixed model equations requires the inverse of the combined relationship matrix {mathbf{H}}, which has a dense matrix block for genotyped animals.MethodsTo avoid inversion of dense matrices, single-step genomic BLUP can be transformed to single-step single nucleotide polymorphism BLUP (SNP-BLUP) which have observed and imputed marker coefficients. Simple block LDL type decompositions of the single-step relationship matrix {mathbf{H}} were derived to obtain different types of linearly equivalent single-step genomic mixed model equations with different sets of reparametrized random effects. For non-genotyped animals, the imputed marker coefficient terms in the single-step SNP-BLUP were calculated on-the-fly during the iterative solution using sparse matrix decompositions without storing the imputed genotypes. Residual polygenic effects were added to genotyped animals and transmitted to non-genotyped animals using relationship coefficients that are similar to imputed genotypes. The relationships were further orthogonalized to improve convergence of iterative methods.ResultsAll presented single-step SNP-BLUP models can be solved efficiently using iterative methods that rely on iteration on data and sparse matrix approaches. The efficiency, accuracy and iteration convergence of the derived mixed model equations were tested with a small dataset that included 73,579 animals of which 2885 were genotyped with 37,526 SNPs.ConclusionsInversion of the large and dense genomic relationship matrix was avoided in single-step evaluation by using fully orthogonalized single-step SNP-BLUP formulations. The number of iterations until convergence was smaller in single-step SNP-BLUP formulations than in the original single-step GBLUP when heritability was low, but increased above that of the original single-step when heritability was high.

Highlights

Single-step genomic best linear unbiased prediction (BLUP) evaluation combines relationship information from pedigree and genomic marker data
When the number of genotyped animals is large, ssGBLUP may become computationally infeasible for practical purposes because it requires the inverses of dense matrices of size equal to the number of genotyped animals, the inverse of the
The original idea in ssSNP-BLUP circumvents the problems in ssGBLUP by predicting or imputing genotypes for non-genotyped animals, and relying on computationally less demanding SNP-based prediction instead of breeding value based prediction

Summary

Introduction

Single-step genomic best linear unbiased prediction (BLUP) evaluation combines relationship information from pedigree and genomic marker data. The inclusion of the genomic information into mixed model equations requires the inverse of the combined relationship matrix H, which has a dense matrix block for genotyped animals. The first model to simultaneously combine genomic information with non-genotyped animal information was single-step best linear unbiased prediction (BLUP) [1, 2] or ssGBLUP. There is no need to construct or invert the genomic relationship matrix Gg. The original idea in ssSNP-BLUP circumvents the problems in ssGBLUP by predicting or imputing genotypes for non-genotyped animals, and relying on computationally less demanding SNP-based prediction instead of breeding value based prediction. An additional advantage is that the marker effect solutions are easier to use for interim predictions

Methods

Results

Conclusion