Genomic Prediction of Average Daily Gain, Back-Fat Thickness, and Loin Muscle Depth Using Different Genomic Tools in Canadian Swine Populations

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.

Weighted Single-Step Genomic Best Linear Unbiased Prediction Method Application for Assessing Pigs on Meat Productivity and Reproduction Traits

“…Ardestani et al (2021) [3] proved the superiority of genomic methods for predicting breeding value over the traditional BLUP procedure in Duroc, Landrace and Yorkshire pigs for meat traits. The greatest estimate reliability for fat thickness, muscle depth and average daily gain was achieved by ssGBLUP and BayesCp.…”

“…However, the increase in reliability was different for each breed studied. According to the authors, this occurrence can be caused by differences in the reference group's volume, the amount of data available for evaluation, and the differences in the breeding selection goals [3].…”

“…The improvement of methods for assessing the farm animals breeding values has two main directions: attracting as much data about the animals and their relatives as possible and developing methods that allow the more efficient use of available information. Thus, genomic evaluation methods have significantly improved the prediction accuracy of the individual genetic value by using marker genotype data [1][2][3]. The next step in this process was the development of mathematical procedures ensuring the most reasonable application of this information and special algorithms to reduce computational and memory costs, in particular, the determination of the optimal weight of the data involved [4,5].…”

Genome-Wide Association Studies, Runs of Homozygosity Analysis, and Copy Number Variation Detection to Identify Reproduction-Related Genes in Bama Xiang Pigs

“…Single nucleotide polymorphism (SNP) arrays can genotype hundreds of thousands of SNPs distributed throughout the genome ( 11 ). Based on the density, the porcine SNP arrays were split into 50K ( 12 ), 60K ( 13 ), and 80K ( 14 ). The beadchips have been widely used for genomic selection ( 15 ), selection signature research ( 14 ), and genome-wide association studies ( 16 ).…”

Genetic Parameter Estimation and Genome-Wide Association Analysis of Social Genetic Effects on Average Daily Gain in Purebreds and Crossbreds

“…In the livestock industry, genomic selection for productive traits is used to rapidly improve livestock. Genomic prediction studies for the ADG trait have been extensively conducted in different pig populations [ 11 , 15 , 16 , 17 , 18 ]. Genomic selection events in purebred pigs have been suggested to increase the selection response for performance in crossbred pigs [ 19 ], and Ask et al [ 11 ] have recently shown that selection for the SGE on ADG in purebred pigs can improve ADG in two-way crossbred pigs.…”