Abstract
Simple SummaryThe individual birth weight (IBW) of pigs is an important trait regarding its relevance to mortality at weaning, sow prolificacy, and growth performance. This study investigates the variance component estimation, informative window regions, and the efficiency of genomic predictions associated with IBW traits in Yorkshire pigs. The low heritability (0.13) is estimated on the basis of a full model including individual genetic, sow genetic, and common environmental effects. Two common window regions of the genome are identified under three different genotyping platforms found within the ARAP2 and TSN genes concerning the IBW trait. The genomic prediction ability is improved using deregressed estimated breeding values including parental information as a response variable despite Bayesian methods and genotyping platforms for the IBW trait in Korean Yorkshire pigs.This study estimates the individual birth weight (IBW) trait heritability and investigates the genomic prediction efficiency using three types of high-density single nucleotide polymorphism (SNP) genotyping panels in Korean Yorkshire pigs. We use 38,864 IBW phenotypic records to identify a suitable model for statistical genetics, where 698 genotypes match our phenotypic records. During our genomic analysis, the deregressed estimated breeding values (DEBVs) and their reliabilities are used as derived response variables from the estimated breeding values (EBVs). Bayesian methods identify the informative regions and perform the genomic prediction using the IBW trait, in which two common significant window regions (SSC8 27 Mb and SSC15 29 Mb) are identified using the three genotyping platforms. Higher prediction ability is observed using the DEBV-including parent average as a response variable, regardless of the SNP genotyping panels and the Bayesian methods, relative to the DEBV-excluding parent average. Hence, we suggest that fine-mapping studies targeting the identified informative regions in this study are necessary to find the causal mutations to improve the IBW trait’s prediction ability. Furthermore, studying the IBW trait using a genomic prediction model with a larger genomic dataset may improve the genomic prediction accuracy in Korean Yorkshire pigs.
Highlights
Litter size and birth weight have been used as representative traits of sow productivity [1].pigs have been selectively bred to focus only on litter size to improve sow productivity [2].According to the 2019 Korean pig industry report of the Korea Animal Improvement Association (KAIA), the total number of living piglets per litter has increased from 10.5 to 12.1 in the past 10 years, indicating that sow prolificacy has improved
Genomic selection (GS) methodology has been widely applied in livestock species, including dairy cattle [16], poultry [17], beef cattle [18], sheep [19], and pigs [20], using genomic information obtained from commercial single nucleotide polymorphism (SNP) genotyping platforms such as Illumina
Through the results of this study, the most optimal statistical model for estimating the birth weight of Yorkshire pigs was identified, after which the genetically superior individuals in terms of their birth weights could be selected through the genetic parameters and breeding values that were estimated by the optimal model
Summary
Litter size and birth weight have been used as representative traits of sow productivity [1].pigs have been selectively bred to focus only on litter size to improve sow productivity [2].According to the 2019 Korean pig industry report of the Korea Animal Improvement Association (KAIA), the total number of living piglets per litter has increased from 10.5 to 12.1 in the past 10 years, indicating that sow prolificacy has improved. It has led to an increase in the occurrence of intra-uterine growth restriction due to the lack of available capacity, oxygen, and nutrients in the uterus and, in turn, causes the birth weight variation to increase [3,4]. Owing to this association, weaning mortality is a major issue for continuously improving sow prolificacy, as it is known to affect the early growth performance. Several studies have reported numerous genetic effects on birth weight [5], where they analyzed 14,226 Yorkshire and 12,313 Landrace sows and identified candidate genes using genome-wide association studies (GWAS) such as SKOR2, SMAD2, VAV3, and NTNG1. Genomic selection (GS) methodology has been widely applied in livestock species, including dairy cattle [16], poultry [17], beef cattle [18], sheep [19], and pigs [20], using genomic information obtained from commercial single nucleotide polymorphism (SNP) genotyping platforms such as Illumina (https://www.illumina.com/products/by-type/microarray-kits/porcine-snp60.html), Neogen GeneSeek (https://genomics.neogen.com/en/ggp-porcine), and Affymetrix
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