Genome-wide Association Study (GWAS) and Its Application for Improving the Genomic Estimated Breeding Values (GEBV) of the Berkshire Pork Quality Traits.

Young-Sup Lee,Seoae Cho,Hyeon Jeong Kim,Mengistie Taye,Sojeong Ka,Youn-Chul Ryu,Hyeonsoo Jeong

doi:10.5713/ajas.15.0287

Abstract

The missing heritability has been a major problem in the analysis of best linear unbiased prediction (BLUP). We introduced the traditional genome-wide association study (GWAS) into the BLUP to improve the heritability estimation. We analyzed eight pork quality traits of the Berkshire breeds using GWAS and BLUP. GWAS detects the putative quantitative trait loci regions given traits. The single nucleotide polymorphisms (SNPs) were obtained using GWAS results with p value <0.01. BLUP analyzed with significant SNPs was much more accurate than that with total genotyped SNPs in terms of narrow-sense heritability. It implies that genomic estimated breeding values (GEBVs) of pork quality traits can be calculated by BLUP via GWAS. The GWAS model was the linear regression using PLINK and BLUP model was the G-BLUP and SNP-GBLUP. The SNP-GBLUP uses SNP-SNP relationship matrix. The BLUP analysis using preprocessing of GWAS can be one of the possible alternatives of solving the missing heritability problem and it can provide alternative BLUP method which can find more accurate GEBVs.

Highlights

Pork is the most widely consumed meat, accounting for 50% of daily meat protein intake, globally (Davis and Lin, 2005)
Various studies have been devoted to the estimation of genetic parameters for pork quality traits to use in selection programs (Leeds, 2005)
Because the most important part of our analysis was the number of single nucleotide polymorphism (SNP), we regarded that the criteria of P-value in Genome-wide Association Study (GWAS) can be modified in the cases of Shear force (SF), fat, water holding capacity (WHC), and MC_A for a better performance of best linear unbiased prediction (BLUP)

Summary

INTRODUCTION

Pork is the most widely consumed meat, accounting for 50% of daily meat protein intake, globally (Davis and Lin, 2005). Genetic selection using best linear unbiased prediction (BLUP) methodologies, so far, have resulted in a. The big compromise of G-BLUP and SNP-GBLUP is single nucleotide polymorphism-genomic best linear unbiased prediction (SNP-GBLUP) (Lee et al, 2014b). It predicts the SNP effects of the given traits. It indicates that the narrow-sense heritability cannot wide association (GWA) analysis with the sex adjusted data. Be achieved satisfactorily in complex diseases and traits with The P-value less than the stringent level of 0.01 was selected a complex inheritance such as human height (Eichler et al, for genome-wide significant autosomal SNPs. 2010; Yang et al, 2010). We analyzed the BLUP by effects such as sex and predicts the random effects such as using only SNPs with p-value under 0.01 in GWAS. The solution of the model usually can be found by using the maximum

MATERIALS AND METHODS

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RESULTS

DISCUSSION

Findings

CONFLICT OF INTEREST