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

Abstract

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.

Vetinformatics from functional genomics to drug discovery: Insights into decoding complex molecular mechanisms of livestock systems in veterinary science

“…Uemoto et al identified six significant quantitative trait loci for immune-related traits in pigs affected by mycoplasma pneumonia of swine using GWAS, revealing novel insights into the genomic elements influencing pig production, respiratory illness, and immunerelated traits (98). Another GWAS-based study identified candidate genes for milk production traits in Korean Holstein cattle and individual birth weight traits in Korean Yorkshire pigs (99,100). Therefore, GWAS-based approaches have potential to decode important and complex traits linked with livestock productivity.…”

A Comparative Study of Machine Learning Methods for Predicting Live Weight of Duroc, Landrace, and Yorkshire Pigs

“…Live weight is an important indicator of livestock productivity and serves as an informative feature for the health, feeding, breeding [1], and selection of livestock [2]. The prediction of live animal weight based on different body characteristics observed during different growth periods for sheep [3], goats [4], chickens [5], ducks [6], rams [7], and cattle [8] has been extensively studied in the literature.…”