Staff Publications

Staff Publications

  • external user (warningwarning)
  • Log in as
  • language uk
  • About

    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 498131
Title Accuracy of genomic prediction using imputed whole-genome sequence data in white layers
Author(s) Heidaritabar, M.; Calus, M.P.L.; Megens, H.J.; Vereijken, A.; Groenen, M.A.M.; Bastiaansen, J.W.M.
Source Journal of Animal Breeding and Genetics (2016). - ISSN 0931-2668 - p. 167 - 179.
DOI http://dx.doi.org/10.1111/jbg.12199
Department(s) Animal Breeding and Genetics
LR - Animal Breeding & Genomics
WIAS
Publication type Refereed Article in a scientific journal
Publication year 2016
Keyword(s) Biological information - Causal mutations - Genomic prediction accuracy - Imputation - Whole-genome sequence
Abstract

There is an increasing interest in using whole-genome sequence data in genomic selection breeding programmes. Prediction of breeding values is expected to be more accurate when whole-genome sequence is used, because the causal mutations are assumed to be in the data. We performed genomic prediction for the number of eggs in white layers using imputed whole-genome resequence data including ~4.6 million SNPs. The prediction accuracies based on sequence data were compared with the accuracies from the 60 K SNP panel. Predictions were based on genomic best linear unbiased prediction (GBLUP) as well as a Bayesian variable selection model (BayesC). Moreover, the prediction accuracy from using different types of variants (synonymous, non-synonymous and non-coding SNPs) was evaluated. Genomic prediction using the 60 K SNP panel resulted in a prediction accuracy of 0.74 when GBLUP was applied. With sequence data, there was a small increase (~1%) in prediction accuracy over the 60 K genotypes. With both 60 K SNP panel and sequence data, GBLUP slightly outperformed BayesC in predicting the breeding values. Selection of SNPs more likely to affect the phenotype (i.e. non-synonymous SNPs) did not improve the accuracy of genomic prediction. The fact that sequence data were based on imputation from a small number of sequenced animals may have limited the potential to improve the prediction accuracy. A small reference population (n = 1004) and possible exclusion of many causal SNPs during quality control can be other possible reasons for limited benefit of sequence data. We expect, however, that the limited improvement is because the 60 K SNP panel was already sufficiently dense to accurately determine the relationships between animals in our data.

Comments
There are no comments yet. You can post the first one!
Post a comment
 
Please log in to use this service. Login as Wageningen University & Research user or guest user in upper right hand corner of this page.