Genome-wide identification of major genes and genomic prediction using high-density and text-mined gene-based SNP panels in Hanwoo (Korean cattle).

Hyo Jun Lee,Seung Hwan Lee,Yoon Ji Chung,Hak Kyo Lee,Duhak Yoon,Dajeong Lim,Dong Won Seo,Sungbong Jang,Shuhong Zhao

doi:10.1371/journal.pone.0241848

Abstract

It was hypothesized that single-nucleotide polymorphisms (SNPs) extracted from text-mined genes could be more tightly related to causal variant for each trait and that differentially weighting of this SNP panel in the GBLUP model could improve the performance of genomic prediction in cattle. Fitting two GRMs constructed by text-mined SNPs and SNPs except text-mined SNPs from 777k SNPs set (exp_777K) as different random effects showed better accuracy than fitting one GRM (Im_777K) for six traits (e.g. backfat thickness: + 0.002, eye muscle area: + 0.014, Warner-Bratzler Shear Force of semimembranosus and longissimus dorsi: + 0.024 and + 0.068, intramuscular fat content of semimembranosus and longissimus dorsi: + 0.008 and + 0.018). These results can suggest that attempts to incorporate text mining into genomic predictions seem valuable, and further study using text mining can be expected to present the significant results.

Highlights

Genomic prediction, which is the first step in genomic selection, is a method for calculating genomic estimated breeding values (GEBVs) using large numbers of genetic markers, such as single-nucleotide polymorphism (SNP), covering the whole genome [1]
The genomic prediction methods that are currently applied to livestock populations use the extent of linkage disequilibrium between markers and quantitative trait loci (QTL) because high-density SNPs increase the chances of co-segregation of markers with causal mutations [2]
The hypothesis of this study was that SNPs extracted from text-mined genes could be in tighter linkage disequilibrium with causal variants for carcass and meat quality traits, and weighting this SNP panel differentially in the Genomic best linear unbiased prediction (GBLUP) model could improve the performance of genomic prediction in cattle

Summary

Introduction

Genomic prediction, which is the first step in genomic selection, is a method for calculating genomic estimated breeding values (GEBVs) using large numbers of genetic markers, such as single-nucleotide polymorphism (SNP), covering the whole genome [1]. The genomic prediction methods that are currently applied to livestock populations use the extent of linkage disequilibrium between markers and quantitative trait loci (QTL) because high-density SNPs increase the chances of co-segregation of markers with causal mutations [2]. Genetic variation in quantitative traits could be influenced by large numbers of loci affecting any given trait with small to moderate effects. There are loci with moderate to large effects due to relatively recently selected mutations [3,4,5]. It is difficult to capture recently selected causal mutations in genomic prediction because the linkage disequilibrium between these.

Methods

Results

Discussion

Conclusion