Genomic Prediction Using Alternative Strategies of Weighted Single-Step Genomic BLUP for Yearling Weight and Carcass Traits in Hanwoo Beef Cattle.

Hossein Mehrban,Mina Park,Chungil Cho,Masoumeh Naserkheil,Taejeong Choi,Noelia Ibáñez-Escriche,Deuk Hwan Lee

doi:10.3390/genes12020266

Abstract

The weighted single-step genomic best linear unbiased prediction (GBLUP) method has been proposed to exploit information from genotyped and non-genotyped relatives, allowing the use of weights for single-nucleotide polymorphism in the construction of the genomic relationship matrix. The purpose of this study was to investigate the accuracy of genetic prediction using the following single-trait best linear unbiased prediction methods in Hanwoo beef cattle: pedigree-based (PBLUP), un-weighted (ssGBLUP), and weighted (WssGBLUP) single-step genomic methods. We also assessed the impact of alternative single and window weighting methods according to their effects on the traits of interest. The data was comprised of 15,796 phenotypic records for yearling weight (YW) and 5622 records for carcass traits (backfat thickness: BFT, carcass weight: CW, eye muscle area: EMA, and marbling score: MS). Also, the genotypic data included 6616 animals for YW and 5134 for carcass traits on the 43,950 single-nucleotide polymorphisms. The ssGBLUP showed significant improvement in genomic prediction accuracy for carcass traits (71%) and yearling weight (99%) compared to the pedigree-based method. The window weighting procedures performed better than single SNP weighting for CW (11%), EMA (11%), MS (3%), and YW (6%), whereas no gain in accuracy was observed for BFT. Besides, the improvement in accuracy between window WssGBLUP and the un-weighted method was low for BFT and MS, while for CW, EMA, and YW resulted in a gain of 22%, 15%, and 20%, respectively, which indicates the presence of relevant quantitative trait loci for these traits. These findings indicate that WssGBLUP is an appropriate method for traits with a large quantitative trait loci effect.

Highlights

In the last decade, the two-step genomic best linear unbiased prediction (GBLUP)method [1] has been the statistical method routinely used for genomic evaluations due to its low computational demand
Comparisons of Alternative WssGBLUP Approaches Over Iterations
The first iteration corresponded with single-step GBLUP (ssGBLUP) where the single-nucleotide polymorphisms (SNPs) weights were equal to one

Summary

Introduction

The two-step genomic best linear unbiased prediction (GBLUP)method [1] has been the statistical method routinely used for genomic evaluations due to its low computational demand. WGBLUP and Bayesian procedures were not considerably different in terms of accuracy for specific traits influenced by few QTL or traits known for being influenced by important QTL [9,13,14]. In both GBLUP and WGBLUP methods, the phenotypic information from non-genotyped animals cannot be utilized, resulting in frequently reduced accuracy and increased bias of these methods [15,16,17,18]

Objectives

Methods

Results

Discussion

Conclusion