# Evaluating Adjusted ssGBLUP Models for Genomic Prediction and Matrix Compatibility in South African Holstein Cattle

## TL;DR

This study shows that adjusted ssGBLUP models improve the accuracy and reduce bias in genomic predictions for milk production traits in South African Holstein cattle.

## Contribution

The study introduces and evaluates adjusted ssGBLUP models that optimize the integration of genomic and pedigree data for more reliable genetic predictions.

## Key findings

- Adjusted ssGBLUP models improved prediction accuracy by 6–7% compared to standard models.
- The adjusted models reduced bias in genomic estimated breeding values for milk, protein, and fat yields.
- Optimizing the blending of pedigree and genomic relationships enhances genetic selection decisions.

## Abstract

A single-step genomic best linear unbiased prediction (ssGBLUP) model can produce biased or less accurate genomic predictions due to incompatibilities between genomic and pedigree information, especially in populations with limited genotypes. This study evaluated the impact of five ssGBLUP models for estimating genomic estimated breeding values (GEBVs) for milk, fat, and protein yields in South African Holstein cattle. The models included the standard ssGBLUP, the ssGBLUP accounting for inbreeding and unknown parent groups, and two adjusted ssGBLUP models incorporating blending and scaling, with and without tuning. The results showed that the adjusted models consistently produced more accurate and less biased GEBVs than the standard model. Therefore, these findings demonstrate that optimizing the integration of genomic and pedigree data can substantially enhance the reliability of genetic predictions and support more effective selection decisions, contributing to faster genetic progress in South African Holstein populations.

In populations with limited genotyping, single-step genomic best linear unbiased predictions (ssGBLUP) can produce biased or less accurate genomic predictions due to incompatibilities between genomic and pedigree relationship matrices. The study evaluated the impact of five alternative ssGBLUP models for genomic predictions of milk, fat, and protein yield production traits in South African Holstein cattle. The dataset included 696,413 milk production records and pedigrees of 541,325 animals. Production traits were 305-day lactation yields for milk, protein, and fat. Genotype data were based on the Illumina 50K chip v3, with 53,218 SNPs. A total of 1221 animals with genotypes and 41,407 SNP markers were in the final dataset. The five models used to estimate genomic estimated breeding values (GEBVs) were the single-step method (ssGBLUP), ssGBLUP accounting for inbreeding (ssGBLUP_Fx), ssGBLUP with unknown parent groups (ssGBLUP_upg), and two ssGBLUP models with blending, tuning, and scaling parameters set to optimum values in constructing the inverse of the unified relationship matrix (ssGBLUP_adjusted). Realized prediction accuracies were highest for ssGBLUP_adjusted models (6–7% improvements compared to ssGBLUP). Accuracy of GEBVs for milk, protein, and fat yields ranged from 0.23, 0.29, and 0.30 for both ssGBLUP and ssGBLUP_Fx, 0.26, 0.32, and 0.34 for ssGBLUP_upg, and 0.29, 0.35, and 0.37 for ssGBLUP_adjusted models, respectively. Corresponding bias, expressed as regression coefficients, ranged from 0.30, 0.31, and 0.36 for ssGBLUP; 0.31, 0.32, and 0.37 for ssGBLUP_Fx; 0.41, 0.44, and 0.49 for ssGBLUP_upg; and 0.44, 0.47, and 0.53 for ssGBLUP_adjusted models, respectively. The improved accuracy and reduced bias observed with the ssGBLUP_adjusted underscores the importance of optimizing the blending of pedigree- and genome-based relationships to achieve more reliable GEBVs, thereby improving selection decisions in Holstein dairy cattle.