Single-Step Genomic Evaluations from Theory to Practice: Using SNP Chips and Sequence Data in BLUPF90

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.

Genomic Prediction Using Alternative Strategies of Weighted Single-Step Genomic BLUP for Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

“…(3) Nonlinear A: the diagonal elements of D matrix (d ii ) for the nonlinear A was defined as where CT (1.125 or 1.25) indicates the departure from normality, is the absolute estimated SNP effect for marker , and is the standard deviation of the vector of estimated SNPs effect [ 3 ]. Besides, the maximum change in SNP variance is limited to five and 20 [ 11 , 34 ].…”

“…where CT (1.125 or 1.25) indicates the departure from normality, | | is the absolute estimated SNP effect for marker , and sd is the standard deviation of the vector of estimated SNPs effect [3]. Besides, the maximum change in SNP variance is limited to five and 20 [11,34]. 4) Largest window: using the concept of SNP-window weights, with weights for a group of (n) SNPs as dii = max( , ).…”

Integrating Single-Step GWAS and Bipartite Networks Reconstruction Provides Novel Insights into Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

“…Consecutive 20 SNPs, which explained 1% or more of the total genetic variance, based on the WssGWAS, were considered as a genomic window associated with the studied traits [ 37 , 38 , 39 ]. The Map Viewer tool for the bovine genome was used to identify positional candidate genes based on the starting and ending coordinates of each window using the UMD 3.1.1 assembly as the reference map ( ).…”

Merging Genomics and Transcriptomics for Predicting Fusarium Head Blight Resistance in Wheat

“…An analogous problem can be found in animal breeding, where genotyping of hundreds of thousands of animals can be likewise cost restrictive. Blending of incomplete genomic with complete pedigree information in a common relationship matrix by the single-step method is therefore a well-established method in livestock improvement programs [ 27 ]. The goals of this study were thus (i) to compare the potential of genomic with transcriptomic prediction using different combinations of gene expressions matrices, and (ii) to assess the merit of blending incomplete transcriptomic with complete genomic data for predicting FHB resistance in winter wheat.…”