Improving Genomic Prediction of Crossbred and Purebred Dairy Cattle.

Majid Khansefid,Kon V Konstantinov,Hans D Daetwyler,Chris Schrooten,Gerben De Jong,Iona M Macleod,Erin O’Connor,Erica G Jewell,Michael E Goddard,Mekonnen Haile-Mariam,Jennie E Pryce

doi:10.3389/fgene.2020.598580

Abstract

This study assessed the accuracy and bias of genomic prediction (GP) in purebred Holstein (H) and Jersey (J) as well as crossbred (H and J) validation cows using different reference sets and prediction strategies. The reference sets were made up of different combinations of 36,695 H and J purebreds and crossbreds. Additionally, the effect of using different sets of marker genotypes on GP was studied (conventional panel: 50k, custom panel enriched with, or close to, causal mutations: XT_50k, and conventional high-density with a limited custom set: pruned HDnGBS). We also compared the use of genomic best linear unbiased prediction (GBLUP) and Bayesian (emBayesR) models, and the traits tested were milk, fat, and protein yields. On average, by including crossbred cows in the reference population, the prediction accuracies increased by 0.01–0.08 and were less biased (regression coefficient closer to 1 by 0.02–0.16), and the benefit was greater for crossbreds compared to purebreds. The accuracy of prediction increased by 0.02 using XT_50k compared to 50k genotypes without affecting the bias. Although using pruned HDnGBS instead of 50k also increased the prediction accuracy by about 0.02, it increased the bias for purebred predictions in emBayesR models. Generally, emBayesR outperformed GBLUP for prediction accuracy when using 50k or pruned HDnGBS genotypes, but the benefits diminished with XT_50k genotypes. Crossbred predictions derived from a joint pure H and J reference were similar in accuracy to crossbred predictions derived from the two separate purebred reference sets and combined proportional to breed composition. However, the latter approach was less biased by 0.13. Most interestingly, using an equalized breed reference instead of an H-dominated reference, on average, reduced the bias of prediction by 0.16–0.19 and increased the accuracy by 0.04 for crossbred and J cows, with a little change in the H accuracy. In conclusion, we observed improved genomic predictions for both crossbreds and purebreds by equalizing breed contributions in a mixed breed reference that included crossbred cows. Furthermore, we demonstrate, that compared to the conventional 50k or high-density panels, our customized set of 50k sequence markers improved or matched the prediction accuracy and reduced bias with both GBLUP and Bayesian models.

Highlights

The interest in providing genomic predictions for crossbred dairy cows has increased especially in recent years (Harris, 2005; Sørensen et al, 2008; VanRaden et al, 2020)
We propose that a single multi-breed reference population including crossbreds, coupled with a set of markers selected to be closer to causal mutations and a Bayesian prediction model, could be beneficial for genomic prediction (GP) in crossbreds while maintaining or improving accuracy in purebreds compared to single breed reference populations
Ref. 4. and Ref. 4 were both based on two separate single-breed reference populations (Ref. 1 and Ref. 2) but where the predictions were proportionally combined for the crossbred prediction and the single reference prediction used for the purebreds

Summary

Introduction

The interest in providing genomic predictions for crossbred dairy cows has increased especially in recent years (Harris, 2005; Sørensen et al, 2008; VanRaden et al, 2020). The establishment of a suitable reference population for crossbred predictions in dairy cattle is challenging because ideally the same reference population should be used to predict the purebreds for more than a single breed. This is because genomic evaluations for dairy cattle are typically very computationally intensive; they are undertaken at a national level for all dairy animals, involve millions of animal records from both purebred and crossbred animals, and are re-analyzed several times per year. Genomic prediction (GP) is often performed within a single purebred reference population because often the accuracy of predictions show high reliability, whereas the accuracy of across-breed GP is low (Kemper et al, 2015)

Methods

Results

Discussion

Conclusion