Detecting the differential genomic variants using cross-population phenotype-associated variant (XP-PAV) of the Landrace and Yorkshire pigs in Korea

Abstract

Although there have been many genome-wide association studies (GWAS) and selective sweep analyses to understand pig genomic regions related to growth performance, these methods considered only the gene effect and selection signal, respectively. In this study, we suggest the cross-population phenotype associated variant (XP-PAV) analysis as a novel method to determine the genomic variants with different effects between the two populations. XP-PAV analysis could reveal the differential genetic variants between the two populations by considering the gene effect and selection signal simultaneously. In this study, we used daily weight gain (DWG) and back fat thickness (BF) as phenotypes and the Landrace and Yorkshire populations were used for XP-PAV analysis. The main aim was to reveal the differential selection by considering the gene effect between Landrace and Yorkshire pigs. In the gene ontology analysis of XP-PAV results, differential selective genes in DWG analysis were involved in the regulation of interleukin-2 production and cell cycle G2/M transition. The protein modification and glycerophospholipid biosynthetic processes were the most enriched terms in the BF analysis. Therefore, we could identify genetic differences for immune and several metabolic pathways between Landrace and Yorkshire breeds using the XP-PAV analysis. In this study, we expect that XP-PAV analysis will play a role in determining useful selective variants with gene effects and provide a new interpretation of the genetic differences between the two populations.

Elucidating breed-specific variants of native pigs in Korea: insights into pig breeds’ genomic characteristics

“…As a dimension reduction method, principal component analysis (PCA) combines multiple features into lower-dimensional features, that can explain most of the variance in a large dataset (Lee et al 2021 ). PCA was conducted using genome-wide complex trait analysis (GCTA) to obtain eigenvalues and eigenvectors (Yang et al 2011 ) and to assess the explanatory power of each breed-specific SNPs, which revealed that breed-specific SNPs can distinguish them from other breeds.…”

A genomic estimated breeding value-assisted reduction method of single nucleotide polymorphism sets: a novel approach for determining the cutoff thresholds in genome-wide association studies and best linear unbiased prediction

“…Genome-wide association studies (GWAS) aim to detect variants of genomic loci associated with complex traits in the population, and common single nucleotide polymorphisms (SNPs) are the main source of these variants. SNPs are used to describe genetic variability and identify candidate genes, and GWAS examine significant SNPs (Lee et al 2021 ; Lee et al 2022 ). Attempts to use linkage analysis for mapping genomic loci that affect complex traits are ubiquitous.…”