Genomic Analysis Using Bayesian Methods under Different Genotyping Platforms in Korean Duroc Pigs

Abstract

Genomic evaluation has been widely applied to several species using commercial single nucleotide polymorphism (SNP) genotyping platforms. This study investigated the informative genomic regions and the efficiency of genomic prediction by using two Bayesian approaches (BayesB and BayesC) under two moderate-density SNP genotyping panels in Korean Duroc pigs. Growth and production records of 1026 individuals were genotyped using two medium-density, SNP genotyping platforms: Illumina60K and GeneSeek80K. These platforms consisted of 61,565 and 68,528 SNP markers, respectively. The deregressed estimated breeding values (DEBVs) derived from estimated breeding values (EBVs) and their reliabilities were taken as response variables. Two Bayesian approaches were implemented to perform the genome-wide association study (GWAS) and genomic prediction. Multiple significant regions for days to 90 kg (DAYS), lean muscle area (LMA), and lean percent (PCL) were detected. The most significant SNP marker, located near the MC4R gene, was detected using GeneSeek80K. Accuracy of genomic predictions was higher using the GeneSeek80K SNP panel for DAYS (Δ2%) and LMA (Δ2–3%) with two response variables, with no gains in accuracy by the Bayesian approaches in four growth and production-related traits. Genomic prediction is best derived from DEBVs including parental information as a response variable between two DEBVs regardless of the genotyping platform and the Bayesian method for genomic prediction accuracy in Korean Duroc pig breeding.

Estimation of Variance Components and Genomic Prediction for Individual Birth Weight Using Three Different Genome-Wide SNP Platforms in Yorkshire Pigs

“…During the current study, we observed an increase in the genomic accuracy when using DEBVincPA as a response variable as compared to DEBVexcPA across the SNP genotyping platforms and Bayesian methods. These results are consistent with previous reports in terms of growth and productive traits in Duroc pigs [40]. During our genomic prediction model, when DEBVexcPA was used as a response variable, it had the advantage of avoiding double counting issues, but the genomic accuracy was higher when DEBVincPA was used as the response variable.…”

“…Contrasting to our expectation, a high extent of linkage disequilibrium (LD) between SNP and quantitative trait loci (QTL) via increasing marker density having the potential to improve prediction accuracy [36,37] was not realized using a high-density genotyping platform such as Axiom porcine660K. However, similar trends also were reported when comparing the prediction accuracy by the density of the genotyping platforms [38][39][40][41]. Additionally, prediction ability on genotyping platforms relies on the extent of causal variants such as QTL, when targeting economical traits, which is in contrast to using dense genotyping platforms [36,42] such as 80K, 650K, and SEQ.…”

“…This can be explained by accounting for the differences in PA among the genotyped animals when including PA after deregressing from the EBVs [44]. Moreover, the obtained genomic prediction accuracy of the IBW trait was relatively small compared to previous studies conducted on the exterior growth and the productive traits of Korean pig populations [20,40]. However, these results were from a relatively small reference size; thus, continuous genetic evaluations using a larger reference size would increase the genomic accuracy of the IBW trait.…”

Genetic architecture and major genes for backfat thickness in pig lines of diverse genetic backgrounds

“…Results from these studies showed that backfat thickness is a polygenic trait that is regulated by a large number of small-effect variants. With the advent of single-nucleotide polymorphism (SNP) genotyping arrays, gene expression analyses, and other high-throughput genotyping technologies, many more candidate genes for backfat thickness have been reported that are involved in very diverse biological functions and metabolic pathways, such as: adipogenesis [ 13 , 14 ]; lipid metabolism (biosynthesis, absorption, transport, catabolism and homeostasis) pathways, including those related to fatty acids and triglycerides [ 13 , 15 , 16 ]; regulation of feed intake and energy homeostasis, through hormone-mediated responses [ 17 – 20 ] or even taste perception [ 21 ]; the adipocytokine signalling pathway [ 17 , 19 ]; the vitamin D metabolic pathway [ 13 ]; and nervous system development and regulation [ 22 ].…”

Functional Analysis of Haplotypes in Bovine PSAP Gene and Their Relationship with Beef Cattle Production Traits

“…Fortunately, over the last decade, bioinformatics has revolutionized livestock breeding. Compared with traditional breeding, molecular breeding has a number of advantages, such as saving time and shortening the generation interval [ 1 , 2 ]. For example, genome-wide association studies (GWAS) have mapped thousands of genetic variants associated with animal development, which is an unprecedented high-resolution genetic characterization of animal breeding [ 3 , 4 , 5 , 6 ].…”