Abstract
Improvement of prediction accuracy of estimated breeding values (EBVs) can lead to increased profitability for swine breeding companies. This study was performed to compare the accuracy of different popular genomic prediction methods and traditional best linear unbiased prediction (BLUP) for future performance of back-fat thickness (BFT), average daily gain (ADG), and loin muscle depth (LMD) in Canadian Duroc, Landrace, and Yorkshire swine breeds. In this study, 17,019 pigs were genotyped using Illumina 60K and Affymetrix 50K panels. After quality control and imputation steps, a total of 41,304, 48,580, and 49,102 single-nucleotide polymorphisms remained for Duroc (n = 6,649), Landrace (n = 5,362), and Yorkshire (n = 5,008) breeds, respectively. The breeding values of animals in the validation groups (n = 392–774) were predicted before performance test using BLUP, BayesC, BayesCπ, genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods. The prediction accuracies were obtained using the correlation between the predicted breeding values and their deregressed EBVs (dEBVs) after performance test. The genomic prediction methods showed higher prediction accuracies than traditional BLUP for all scenarios. Although the accuracies of genomic prediction methods were not significantly (P > 0.05) different, ssGBLUP was the most accurate method for Duroc-ADG, Duroc-LMD, Landrace-BFT, Landrace-ADG, and Yorkshire-BFT scenarios, and BayesCπ was the most accurate method for Duroc-BFT, Landrace-LMD, and Yorkshire-ADG scenarios. Furthermore, BayesCπ method was the least biased method for Duroc-LMD, Landrace-BFT, Landrace-ADG, Yorkshire-BFT, and Yorkshire-ADG scenarios. Our findings can be beneficial for accelerating the genetic progress of BFT, ADG, and LMD in Canadian swine populations by selecting more accurate and unbiased genomic prediction methods.
Highlights
In the pork industry, genetics has a pivotal role in improving the economically important traits, such as meat quality, growth, and reproductive performance (Badke et al, 2014)
Finding an accurate and unbiased genomic prediction method can be a lucrative strategy for genetic improvement of key traits in livestock species (Mrode et al, 2018)
The accuracies ranged from 13.9% (Duroc-parent average EBV (PA)) to 52.7% (Landrace-single-step GBLUP (ssGBLUP)) for back-fat thickness (BFT), from 5.7% (Duroc-PA) to 34.5% (Landrace-ssGBLUP) for average daily gain (ADG), and from 3.7% (DurocPA) to 25.1% (Landrace-BayesCπ) for loin muscle depth (LMD)
Summary
Genetics has a pivotal role in improving the economically important traits, such as meat quality, growth, and reproductive performance (Badke et al, 2014). Evaluation methods based on genotypic information or genomic evaluation are being used increasingly in modern pig breeding industry (Badke et al, 2014) because of their accelerative role in genetic improvement through augmentation of predictive ability. The single-step GBLUP (ssGBLUP) method was considerably more accurate than GBLUP and BayesR for growth traits in a larger population (n = 2,084) of Yorkshire breed (Song et al, 2017). The differences between these approaches are their assumptions, for example, about the distribution of marker effects (Hayes and Goddard, 2001). Determining the most accurate method for genomic evaluation of different swine breeds is an important step of selective breeding
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