Abstract
Single-step genomic evaluation became a standard procedure in livestock breeding, and the main reason is the ability to combine all pedigree, phenotypes, and genotypes available into one single evaluation, without the need of post-analysis processing. Therefore, the incorporation of data on genotyped and non-genotyped animals in this method is straightforward. Since 2009, two main implementations of single-step were proposed. One is called single-step genomic best linear unbiased prediction (ssGBLUP) and uses single nucleotide polymorphism (SNP) to construct the genomic relationship matrix; the other is the single-step Bayesian regression (ssBR), which is a marker effect model. Under the same assumptions, both models are equivalent. In this review, we focus solely on ssGBLUP. The implementation of ssGBLUP into the BLUPF90 software suite was done in 2009, and since then, several changes were made to make ssGBLUP flexible to any model, number of traits, number of phenotypes, and number of genotyped animals. Single-step GBLUP from the BLUPF90 software suite has been used for genomic evaluations worldwide. In this review, we will show theoretical developments and numerical examples of ssGBLUP using SNP data from regular chips to sequence data.
Highlights
In the early 1980s, Soller et al [1] hypothesized that DNA markers like RFLPs would be beneficial in constructing more precise genetic relationships, followed by parentage determination, and the identification of quantitative trait loci (QTL)
Several adjustments were proposed, especially in dairy cattle, to make EBV comparable to genomic EBV (GEBV) under multistep evaluations [11,12], and it was acknowledged that multi-step methods would eventually lead to bias predictions because best linear unbiased prediction (BLUP) predictions would ignore the effects of genomic selection [13]
Advantages of single-step genomic best linear unbiased prediction (ssGBLUP) include simplicity of use, simultaneous fit of genomic information and estimation of fixed effects [10], relatively higher accuracy than multistep methods [41,42,43,44,45], potential to account for pre-selection bias as all pedigree, phenotypic, and genomic information can be included in the model [12,13], and can be extended to any model
Summary
In the early 1980s, Soller et al [1] hypothesized that DNA markers like RFLPs (restriction fragment length polymorphisms) would be beneficial in constructing more precise genetic relationships, followed by parentage determination, and the identification of quantitative trait loci (QTL). Several adjustments were proposed, especially in dairy cattle, to make EBV comparable to GEBV under multistep evaluations [11,12], and it was acknowledged that multi-step methods would eventually lead to bias predictions because BLUP predictions would ignore the effects of genomic selection [13]. Intending to solve these problems and to reduce the burden in obtaining genomic predictions, Misztal et al [14] proposed a method that combines phenotypes, pedigree, and genotypes into a single evaluation. Examples are ASREML [27], Wombat [28], Mix99 [29], DMU [30], MTG2 [31], GCTA [32], among others
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