Genome-assisted prediction of a quantitative trait measured in parents and progeny: application to food conversion rate in chickens

Oscar González-Recio,Daniel Gianola,Kent A Weigel,Andreas Kranis,Guilherme Jm Rosa

doi:10.1186/1297-9686-41-3

Abstract

Accuracy of prediction of yet-to-be observed phenotypes for food conversion rate (FCR) in broilers was studied in a genome-assisted selection context. Data consisted of FCR measured on the progeny of 394 sires with SNP information. A Bayesian regression model (Bayes A) and a semi-parametric approach (Reproducing kernel Hilbert Spaces regression, RKHS) using all available SNPs (p = 3481) were compared with a standard linear model in which future performance was predicted using pedigree indexes in the absence of genomic data. The RKHS regression was also tested on several sets of pre-selected SNPs (p = 400) using alternative measures of the information gain provided by the SNPs. All analyses were performed using 333 genotyped sires as training set, and predictions were made on 61 birds as testing set, which were sons of sires in the training set. Accuracy of prediction was measured as the Spearman correlation () between observed and predicted phenotype, with its confidence interval assessed through a bootstrap approach. A large improvement of genome-assisted prediction (up to an almost 4-fold increase in accuracy) was found relative to pedigree index. Bayes A and RKHS regression were equally accurate ( = 0.27) when all 3481 SNPs were included in the model. However, RKHS with 400 pre-selected informative SNPs was more accurate than Bayes A with all SNPs.

Highlights

Genome-wide association studies of diseases and complex traits [1] have permeated into animal breeding, and genome-assisted selection has become a major focus of research [2,3]
The objective of this study was to compare the ability of Bayesian regression model (Bayes A) regression and of semi-parametric (RKHS) regression to predict yet-to-be observed phenotypes, using field data on food conversion rate (FCR) in a two-generation setting
Analyses using reproducing kernel Hilbert spaces (RKHS) regression on 400 pre-selected SNPs produced a slightly smaller posterior mean of the residual variance than analyses based on all 3481 SNPs

Summary

Introduction

Genome-wide association studies of diseases and complex traits [1] have permeated into animal breeding, and genome-assisted selection has become a major focus of research [2,3]. Bayesian regression methods, such as Bayes A and Bayes B [2], or the special case of Bayes A described by Xu [5] have recently gained attention. All of these procedures involve strong assumptions a priori. Non-parametric methods have been suggested as an alternative for predicting genomic breeding values, because these methods may require weaker assumptions when modeling complex quantitative traits [6]

Objectives

Methods

Results

Conclusion