Across‐country genomic predictions in Norwegian and New Zealand Composite sheep populations with similar development history

Abstract

The goal of this study was to assess the feasibility of across‐country genomic predictions in Norwegian White Sheep (NWS) and New Zealand Composite (NZC) sheep populations with similar development history. Different training populations were evaluated (i.e., including only NWS or NZC, or combining both populations). Predictions were performed using the actual phenotypes (normalized) and the single‐step GBLUP via Bayesian inference. Genotyped NWS animals born in 2016 (N = 267) were used to assess the accuracy and bias of genomic estimated breeding values (GEBVs) predicted for birth weight (BW), weaning weight (WW), carcass weight (CW), EUROP carcass classification (EUC), and EUROP fat grading (EUF). The accuracy and bias of GEBVs differed across traits and training population used. For instance, the GEBV accuracies ranged from 0.13 (BW) to 0.44 (EUC) for GEBVs predicted including only NWS, from 0.06 (BW) to 0.15 (CW) when including only NZC, and from 0.10 (BW) to 0.41 (EUC) when including both NWS and NZC animals in the training population. The regression coefficients used to assess the spread of GEBVs (bias) ranged from 0.26 (BW) to 0.64 (EUF) for only NWS, 0.10 (EUC) to 0.52 (CW) for only NZC, and from 0.42 (WW) to 2.23 (EUC) for both NWS and NZC in the training population. Our findings suggest that across‐country genomic predictions based on ssGBLUP might be possible for NWS and NZC, especially for novel traits.

SNP‐ and haplotype‐based single‐step genomic predictions for body weight, wool, and reproductive traits in North American Rambouillet sheep

“…Prediction bias, as a property of the method and the population under evaluation, is the expectation of the difference between average true and predicted breeding value; bias is zero under ideal conditions and can be approximated by the difference between the average (G)EBV in the whole and partial data sets (Legarra & Reverter, 2018 ). The observation that GEBV and EBV for most of the scenarios across traits were over‐estimated in the Rambouillet sheep was consistent with other studies (Brown et al, 2018 ; Moghaddar et al, 2019 ; Oliveira et al, 2021 ). Our conclusions regarding the benefit of including genomic information in prediction based on dispersion followed the same pattern observed for the GEBV prediction accuracies; this was likely because they are affected by similar factors.…”

“…The genomic prediction accuracies observed in our study were within the range for most of the economic traits in sheep, which is between 0.20 and 0.50 according to Brown et al (2018), especially when using of 0.95 to construct the G matrix. Oliveira et al (2021) observed prediction accuracies for BWT ranging from 0.06 to 0.13 using H‐BLUP for Norwegian White and New Zealand Composite sheep populations. Unlike what was observed in the current study, Moghaddar et al (2019) showed accuracies for genomic predictions ranging between 0.40 to 0.60 for PWT, 0.30 to 0.40 for yearling clean fleece weight, and 0.30 to 0.50 for YFD using BayesR and GBLUP for purebred Merino and crosses between Merino and Border Leicester.…”

“…The regression coefficient of the adjusted (or corrected) phenotypes or EBV on the GEBV is usually used to measure the “bias” of GEBV (Brown et al, 2018 ; Gao et al, 2012 ; Moghaddar et al, 2019 ; Oliveira et al, 2021 ). This measure was assessed as dispersion in our study, as it represents how the GEBV were inflated or deflated (over‐ or under‐estimated, respectively).…”

Including genomic information in the genetic evaluation of production and reproduction traits in South African Merino sheep

“…The reference populations and pipelines for analysis has already been well established for these breeding programmes (Brown et al, 2018; Swan et al, 2014), but across country genomic prediction (e.g. de Oliveira et al, 2021) is yet to be investigated for Merino sheep.…”

Genetic Characterization and Population Connectedness of North American and European Dairy Goats

“…Furthermore, combining data from several populations is only feasible if they are genetically related (Lund et al, 2014;Rezende et al, 2020). However, recent studies in Norwegian and New Zealand sheep with similar development history, but reduced recent exchange of genetic material, have reported that collaborative genomic analyses could still be feasible (Oliveira et al, 2020;Oliveira et al, 2022).…”